diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..7115cd7 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,25 @@ +name: ci + +on: + push: + branches: [main] + pull_request: + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v6 + - run: uvx ruff@0.12.12 check . + - run: uvx ruff@0.12.12 format --check . + + test: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v6 + with: + python-version: "3.12" + - run: uv sync --frozen --group dev + - run: uv run pytest -q diff --git a/README.md b/README.md index 8eca4cb..565e850 100644 --- a/README.md +++ b/README.md @@ -1,61 +1,103 @@ -# plugrl-rlinf +# plugrl-bridges -[RLinf](https://github.com/RLinf/RLinf) trains environments that PlugRL env +Trainers that were not written for PlugRL train environments that PlugRL env clients run, through the [PlugRL protocol](https://github.com/PlugRL/plugrl-protocol). -RLinf runs its environments inside its own cluster, as Ray workers with -RLinf's Python and torch. This bridge is an RLinf environment type, -`plugrl`, whose environments are anywhere else that can reach a port: -- a `plugrl-env-client` on a machine with no GPU and no RLinf; -- the C++ client in plugrl-protocol, which links nothing but the C++ - runtime; -- anything else that speaks the protocol. +An env client speaks the protocol from wherever its environment has to +live: +- another process, or another machine with no GPU and no deep learning stack; +- a program in another language: the C++ client in plugrl-protocol links + nothing but the C++ runtime. -RLinf sees an ordinary vectorised environment. The client sees an ordinary -PlugRL training server. +This package puts such clients behind the environment interface a trainer +already has: + +| adapter | for | interface | +| --- | --- | --- | +| `plugrl_bridges.rlinf.PlugRLEnv` | [RLinf](https://github.com/RLinf/RLinf) | RLinf's env contract, `env_type: plugrl` | +| `plugrl_bridges.gym.PlugRLVectorEnv` | CleanRL, TorchRL, your own loop | `gymnasium.vector.VectorEnv` | +| `plugrl_bridges.sb3.PlugRLVecEnv` | [Stable-Baselines3](https://github.com/DLR-RM/stable-baselines3) | SB3's `VecEnv` | + +```python +import gymnasium as gym, numpy as np +from stable_baselines3 import PPO +from plugrl_bridges.sb3 import PlugRLVecEnv + +env = PlugRLVecEnv(num_envs=16, port=8800, + action_space=gym.spaces.Box(-1, 1, (6,), np.float32)) +PPO("MlpPolicy", env).learn(409_600) +``` + +The adapter blocks until 16 environments have connected, and reads the +observation space off what they sent. ## How it works -`PlugRLEnv` implements RLinf's environment contract, `reset()` and -`chunk_step(actions)` under `auto_reset`. It does so on top of -`BridgeServer`, a PlugRL server that runs in a thread of RLinf's -environment worker: -- **Reset** waits until every slot has asked for its first action. -- **Each `chunk_step`** sends every client its actions. It returns once each - client has sent its feedback and asked again. -- **Episodes ending.** For each env whose episode ended, the terminal - observation goes in `infos["final_observation"]`, and the episode's return - and length in `infos["final_info"]["episode"]`. RLinf's own simulators do - the same. -- **Features.** The server offers `reuse-feedback-obs`. - -It is a **lockstep** server: RLinf steps all of its environments together, -so every infer and every feedback on a connection must name all of that +All three adapters sit on one `BridgeServer`, a PlugRL protocol server that +the trainer steps from its own thread: +- **Slots.** Each env client takes slots in the order it connects. +- **Steps.** Each step sends every client its actions. It returns once each + client has fed back and asked for its next action. +- **Episode ends.** For each env whose episode ended, the step returns its + reset observation, with the terminal one beside it, the way each trainer + expects: `final_observation` for RLinf, `final_obs` under gymnasium's + SAME_STEP autoreset, `terminal_observation` for SB3. Episode returns and + lengths are counted by the bridge, from the raw rewards. +- **Episode log.** With `episode_log=PATH` (RLinf: `env.train.episode_log`), + or `PLUGRL_BRIDGES_EPISODE_LOG` set, the server writes one CSV line per + episode: `step,slot,episode,return,length`. It is the same record + whichever trainer steps the bridge, so runs of different trainers can be + compared on it. +- **Features.** The server offers `reuse-feedback-obs`, so a client that + uses it sends each observation once. + +It is a **lockstep** server: these trainers step all their environments +together, so every infer and feedback on a connection must name all of that connection's envs. A client running one-step chunks always does, and the -server's metadata asks for `action_horizon: 1`. A client that breaks -lockstep is closed for a resync with the reason. A client that disconnects -mid-run stops the run (`ClientLost`). The bridge never fills in a step it -did not receive. +server's metadata says `action_horizon: 1`. It never fills in a step it did +not receive: +- a client that breaks lockstep is closed for a resync; +- a client too many is told to stop; +- a client lost mid-run stops the run (`ClientLost`). -## Install +Two things a trainer cannot do through it: +- **Reset environments itself.** The protocol has no way for a server to + ask, so `reset()` returns the observations the next step starts from. +- **Use per-env methods.** Those environments are reached only through the + protocol. -RLinf has a closed list of environment types, so it needs a two-line patch -to know `plugrl`. The patch is in `patches/`. Then: +**gymnasium notes.** As of gymnasium 1.3: +- Its vector observation wrappers refuse SAME_STEP autoreset, so + `plugrl_bridges.gym.NormalizeObservation` is provided. +- Its vector `RecordEpisodeStatistics` counts as if autoreset were + NEXT_STEP. Read `infos["episode"]` from the adapter instead. + +## Install ```bash -pip install -e /path/to/RLinf # with the patch applied -pip install plugrl-rlinf +pip install "plugrl-bridges[sb3]" # or [gym], [rlinf] ``` +RLinf has a closed list of environment types, so it needs a two-hunk patch +to know `plugrl` (`patches/`). + +## Examples + +- `examples/sb3/ppo.py`: SB3's PPO. +- `examples/cleanrl/ppo_continuous_action.py`: CleanRL's PPO script, with + only its environment creation and episode logging changed (MIT, + `LICENSE-CLEANRL`). +- `examples/rlinf/`: RLinf PPO-MLP configs. +- `examples/run_bridged.sh`: one run of any of them, with 16 env clients. +- `examples/smoke_probe.py`, `examples/drive_probe.py`: checks against real + clients on SPEC.md's probe environment. + ## Tests ```bash uv run --group dev pytest ``` -The tests drive `PlugRLEnv` the way RLinf's environment worker does, against -fake clients that speak the real protocol. - ## License -Apache-2.0. +Apache-2.0. `examples/cleanrl/` is CleanRL's, under MIT. diff --git a/examples/cleanrl/LICENSE-CLEANRL b/examples/cleanrl/LICENSE-CLEANRL new file mode 100644 index 0000000..c4e5623 --- /dev/null +++ b/examples/cleanrl/LICENSE-CLEANRL @@ -0,0 +1,316 @@ +MIT License + +Copyright (c) 2019 CleanRL developers + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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IN NO EVENT SHALL +THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +DEALINGS IN THE SOFTWARE. + +-------------------------------------------------------------------------------- + +Code in `cleanrl/qdagger_dqn_atari_impalacnn.py` and `cleanrl/qdagger_dqn_atari_jax_impalacnn.py` are adapted from https://github.com/google-research/reincarnating_rl + +**NOTE: the original repo did not fill out the copyright section in their license +so the following copyright notice is copied as is per the license requirement. +See https://github.com/google-research/reincarnating_rl/blob/a1d402f48a9f8658ca6aa0ddf416ab391745ff2c/LICENSE#L189 + + +Copyright [yyyy] [name of copyright owner] + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. diff --git a/examples/cleanrl/ppo_continuous_action.py b/examples/cleanrl/ppo_continuous_action.py new file mode 100644 index 0000000..b4bff78 --- /dev/null +++ b/examples/cleanrl/ppo_continuous_action.py @@ -0,0 +1,354 @@ +# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_continuous_actionpy +# +# PLUGRL: CleanRL's ppo_continuous_action.py (MIT, LICENSE-CLEANRL here), with +# its environments replaced by PlugRL env clients behind plugrl-bridges' +# gymnasium VectorEnv. Every change is marked PLUGRL; the algorithm is not +# touched. Start the env clients against --port (one env each, one-step +# chunks), for example +# plugrl-run-env-client mujoco-v1 --server-host HOST --server-port 8800 \ +# --num-procs 16 --num-envs 1 --runner.replan-steps 1 +# then +# python ppo_continuous_action.py --num-envs 16 --port 8800 --action-dim 6 +import os +import random +import time +from dataclasses import dataclass + +import gymnasium as gym +import numpy as np +import torch +import torch.nn as nn +import torch.optim as optim +import tyro +from torch.distributions.normal import Normal +from torch.utils.tensorboard import SummaryWriter + +from plugrl_bridges.gym import NormalizeObservation, PlugRLVectorEnv # PLUGRL + + +@dataclass +class Args: + exp_name: str = os.path.basename(__file__)[: -len(".py")] + """the name of this experiment""" + seed: int = 1 + """seed of the experiment""" + torch_deterministic: bool = True + """if toggled, `torch.backends.cudnn.deterministic=False`""" + cuda: bool = True + """if toggled, cuda will be enabled by default""" + track: bool = False + """if toggled, this experiment will be tracked with Weights and Biases""" + wandb_project_name: str = "cleanRL" + """the wandb's project name""" + wandb_entity: str = None + """the entity (team) of wandb's project""" + capture_video: bool = False + """whether to capture videos of the agent performances (check out `videos` folder)""" + save_model: bool = False + """whether to save model into the `runs/{run_name}` folder""" + upload_model: bool = False + """whether to upload the saved model to huggingface""" + hf_entity: str = "" + """the user or org name of the model repository from the Hugging Face Hub""" + + # Algorithm specific arguments + env_id: str = "plugrl" + """the id of the environment (PLUGRL: a label only; the clients choose the env)""" + port: int = 8800 + """PLUGRL: the port the env clients connect to""" + host: str = "0.0.0.0" + """PLUGRL: the address to listen on""" + action_dim: int = 6 + """PLUGRL: the env's action dimension (the protocol does not carry it)""" + action_low: float = -1.0 + """PLUGRL: the env's action bound, below""" + action_high: float = 1.0 + """PLUGRL: the env's action bound, above""" + state_keys: tuple[str, ...] = ("obs",) + """PLUGRL: the client's states to concatenate into the observation""" + total_timesteps: int = 1000000 + """total timesteps of the experiments""" + learning_rate: float = 3e-4 + """the learning rate of the optimizer""" + num_envs: int = 1 + """the number of parallel game environments""" + num_steps: int = 2048 + """the number of steps to run in each environment per policy rollout""" + anneal_lr: bool = True + """Toggle learning rate annealing for policy and value networks""" + gamma: float = 0.99 + """the discount factor gamma""" + gae_lambda: float = 0.95 + """the lambda for the general advantage estimation""" + num_minibatches: int = 32 + """the number of mini-batches""" + update_epochs: int = 10 + """the K epochs to update the policy""" + norm_adv: bool = True + """Toggles advantages normalization""" + clip_coef: float = 0.2 + """the surrogate clipping coefficient""" + clip_vloss: bool = True + """Toggles whether or not to use a clipped loss for the value function, as per the paper.""" + ent_coef: float = 0.0 + """coefficient of the entropy""" + vf_coef: float = 0.5 + """coefficient of the value function""" + max_grad_norm: float = 0.5 + """the maximum norm for the gradient clipping""" + target_kl: float = None + """the target KL divergence threshold""" + + # to be filled in runtime + batch_size: int = 0 + """the batch size (computed in runtime)""" + minibatch_size: int = 0 + """the mini-batch size (computed in runtime)""" + num_iterations: int = 0 + """the number of iterations (computed in runtime)""" + + +def layer_init(layer, std=np.sqrt(2), bias_const=0.0): + torch.nn.init.orthogonal_(layer.weight, std) + torch.nn.init.constant_(layer.bias, bias_const) + return layer + + +class Agent(nn.Module): + def __init__(self, envs): + super().__init__() + self.critic = nn.Sequential( + layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)), + nn.Tanh(), + layer_init(nn.Linear(64, 64)), + nn.Tanh(), + layer_init(nn.Linear(64, 1), std=1.0), + ) + self.actor_mean = nn.Sequential( + layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)), + nn.Tanh(), + layer_init(nn.Linear(64, 64)), + nn.Tanh(), + layer_init(nn.Linear(64, np.prod(envs.single_action_space.shape)), std=0.01), + ) + self.actor_logstd = nn.Parameter(torch.zeros(1, np.prod(envs.single_action_space.shape))) + + def get_value(self, x): + return self.critic(x) + + def get_action_and_value(self, x, action=None): + action_mean = self.actor_mean(x) + action_logstd = self.actor_logstd.expand_as(action_mean) + action_std = torch.exp(action_logstd) + probs = Normal(action_mean, action_std) + if action is None: + action = probs.sample() + return action, probs.log_prob(action).sum(1), probs.entropy().sum(1), self.critic(x) + + +if __name__ == "__main__": + args = tyro.cli(Args) + args.batch_size = int(args.num_envs * args.num_steps) + args.minibatch_size = int(args.batch_size // args.num_minibatches) + args.num_iterations = args.total_timesteps // args.batch_size + run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}" + if args.track: + import wandb + + wandb.init( + project=args.wandb_project_name, + entity=args.wandb_entity, + sync_tensorboard=True, + config=vars(args), + name=run_name, + monitor_gym=True, + save_code=True, + ) + writer = SummaryWriter(f"runs/{run_name}") + writer.add_text( + "hyperparameters", + "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])), + ) + + # TRY NOT TO MODIFY: seeding + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + torch.backends.cudnn.deterministic = args.torch_deterministic + + device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu") + + # env setup + # PLUGRL: the envs are PlugRL env clients, reached through the bridge. + # make_env's per-env wrappers become vector wrappers. Two are not + # gymnasium's: RecordEpisodeStatistics is dropped, because the bridge + # reports episodes itself, and NormalizeObservation (with TransformObservation's + # clip) is plugrl_bridges.gym's, because gymnasium's vector observation + # wrappers refuse SAME_STEP autoreset. See plugrl_bridges.gym for both. + envs = PlugRLVectorEnv( + num_envs=args.num_envs, + action_space=gym.spaces.Box(args.action_low, args.action_high, (args.action_dim,), np.float32), + port=args.port, + host=args.host, + state_keys=args.state_keys, + ) + envs = gym.wrappers.vector.ClipAction(envs) + envs = NormalizeObservation(envs, clip=10.0) + envs = gym.wrappers.vector.NormalizeReward(envs, gamma=args.gamma) + envs = gym.wrappers.vector.TransformReward(envs, lambda reward: np.clip(reward, -10, 10)) + assert isinstance(envs.single_action_space, gym.spaces.Box), "only continuous action space is supported" + + agent = Agent(envs).to(device) + optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5) + + # ALGO Logic: Storage setup + obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device) + actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device) + logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device) + rewards = torch.zeros((args.num_steps, args.num_envs)).to(device) + dones = torch.zeros((args.num_steps, args.num_envs)).to(device) + values = torch.zeros((args.num_steps, args.num_envs)).to(device) + + # TRY NOT TO MODIFY: start the game + global_step = 0 + start_time = time.time() + next_obs, _ = envs.reset(seed=args.seed) + next_obs = torch.Tensor(next_obs).to(device) + next_done = torch.zeros(args.num_envs).to(device) + + for iteration in range(1, args.num_iterations + 1): + # Annealing the rate if instructed to do so. + if args.anneal_lr: + frac = 1.0 - (iteration - 1.0) / args.num_iterations + lrnow = frac * args.learning_rate + optimizer.param_groups[0]["lr"] = lrnow + + for step in range(0, args.num_steps): + global_step += args.num_envs + obs[step] = next_obs + dones[step] = next_done + + # ALGO LOGIC: action logic + with torch.no_grad(): + action, logprob, _, value = agent.get_action_and_value(next_obs) + values[step] = value.flatten() + actions[step] = action + logprobs[step] = logprob + + # TRY NOT TO MODIFY: execute the game and log data. + next_obs, reward, terminations, truncations, infos = envs.step(action.cpu().numpy()) + next_done = np.logical_or(terminations, truncations) + rewards[step] = torch.tensor(reward).to(device).view(-1) + next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(next_done).to(device) + + # PLUGRL: gymnasium 1.x's layout, infos["episode"] masked by + # infos["_episode"]. (The list-of-dicts final_info read here + # before is gymnasium 0.29's; under 1.x it logs nothing.) + if "_episode" in infos: + for i in np.flatnonzero(infos["_episode"]): + print(f"global_step={global_step}, episodic_return={infos['episode']['r'][i]}") + writer.add_scalar("charts/episodic_return", infos["episode"]["r"][i], global_step) + writer.add_scalar("charts/episodic_length", infos["episode"]["l"][i], global_step) + + # bootstrap value if not done + with torch.no_grad(): + next_value = agent.get_value(next_obs).reshape(1, -1) + advantages = torch.zeros_like(rewards).to(device) + lastgaelam = 0 + for t in reversed(range(args.num_steps)): + if t == args.num_steps - 1: + nextnonterminal = 1.0 - next_done + nextvalues = next_value + else: + nextnonterminal = 1.0 - dones[t + 1] + nextvalues = values[t + 1] + delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t] + advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam + returns = advantages + values + + # flatten the batch + b_obs = obs.reshape((-1,) + envs.single_observation_space.shape) + b_logprobs = logprobs.reshape(-1) + b_actions = actions.reshape((-1,) + envs.single_action_space.shape) + b_advantages = advantages.reshape(-1) + b_returns = returns.reshape(-1) + b_values = values.reshape(-1) + + # Optimizing the policy and value network + b_inds = np.arange(args.batch_size) + clipfracs = [] + for epoch in range(args.update_epochs): + np.random.shuffle(b_inds) + for start in range(0, args.batch_size, args.minibatch_size): + end = start + args.minibatch_size + mb_inds = b_inds[start:end] + + _, newlogprob, entropy, newvalue = agent.get_action_and_value(b_obs[mb_inds], b_actions[mb_inds]) + logratio = newlogprob - b_logprobs[mb_inds] + ratio = logratio.exp() + + with torch.no_grad(): + # calculate approx_kl http://joschu.net/blog/kl-approx.html + old_approx_kl = (-logratio).mean() + approx_kl = ((ratio - 1) - logratio).mean() + clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()] + + mb_advantages = b_advantages[mb_inds] + if args.norm_adv: + mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8) + + # Policy loss + pg_loss1 = -mb_advantages * ratio + pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1 + args.clip_coef) + pg_loss = torch.max(pg_loss1, pg_loss2).mean() + + # Value loss + newvalue = newvalue.view(-1) + if args.clip_vloss: + v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2 + v_clipped = b_values[mb_inds] + torch.clamp( + newvalue - b_values[mb_inds], + -args.clip_coef, + args.clip_coef, + ) + v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2 + v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped) + v_loss = 0.5 * v_loss_max.mean() + else: + v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean() + + entropy_loss = entropy.mean() + loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef + + optimizer.zero_grad() + loss.backward() + nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm) + optimizer.step() + + if args.target_kl is not None and approx_kl > args.target_kl: + break + + y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy() + var_y = np.var(y_true) + explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y + + # TRY NOT TO MODIFY: record rewards for plotting purposes + writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step) + writer.add_scalar("losses/value_loss", v_loss.item(), global_step) + writer.add_scalar("losses/policy_loss", pg_loss.item(), global_step) + writer.add_scalar("losses/entropy", entropy_loss.item(), global_step) + writer.add_scalar("losses/old_approx_kl", old_approx_kl.item(), global_step) + writer.add_scalar("losses/approx_kl", approx_kl.item(), global_step) + writer.add_scalar("losses/clipfrac", np.mean(clipfracs), global_step) + writer.add_scalar("losses/explained_variance", explained_var, global_step) + print("SPS:", int(global_step / (time.time() - start_time))) + writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step) + + if args.save_model: + model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model" + torch.save(agent.state_dict(), model_path) + print(f"model saved to {model_path}") + # PLUGRL: CleanRL's evaluation here re-creates envs with gym.make; dropped. + + envs.close() + writer.close() diff --git a/examples/drive_probe.py b/examples/drive_probe.py index 3f9ca12..a83109a 100644 --- a/examples/drive_probe.py +++ b/examples/drive_probe.py @@ -25,7 +25,7 @@ import torch -from plugrl_rlinf.env import PlugRLEnv +from plugrl_bridges.rlinf import PlugRLEnv LENGTHS = [ 3, diff --git a/examples/rlinf/config/env/plugrl_halfcheetah.yaml b/examples/rlinf/config/env/plugrl_halfcheetah.yaml index 3532ed3..67d9bf6 100644 --- a/examples/rlinf/config/env/plugrl_halfcheetah.yaml +++ b/examples/rlinf/config/env/plugrl_halfcheetah.yaml @@ -14,6 +14,7 @@ action_dim: 6 action_low: -1.0 action_high: 1.0 timeout: 600 +episode_log: null # a CSV path: one line per episode, written by the bridge seed: 0 total_num_envs: null diff --git a/examples/rlinf/config/env/plugrl_pendulum.yaml b/examples/rlinf/config/env/plugrl_pendulum.yaml new file mode 100644 index 0000000..1ab2f32 --- /dev/null +++ b/examples/rlinf/config/env/plugrl_pendulum.yaml @@ -0,0 +1,33 @@ +# RLinf env config for env_type plugrl: Pendulum, run by the C++ client of +# plugrl-server's E44 (pendulum_client.cpp: one env per process, its own copy +# of gymnasium's Pendulum-v1, torque clipped to [-2, 2]). Start 16 of them: +# for s in $(seq 16); do ./pendulum_client HOST PORT $s & done +env_type: plugrl + +host: 0.0.0.0 +port: 8800 +state_keys: ["obs"] +image_key: null +action_dim: 1 +action_low: -2.0 +action_high: 2.0 +timeout: 600 +episode_log: null # a CSV path: one line per episode, written by the bridge + +seed: 0 +total_num_envs: null +group_size: 1 + +auto_reset: True +ignore_terminations: False +max_steps_per_rollout_epoch: 256 +max_episode_steps: 200 + +use_rel_reward: False +reward_coef: 1.0 +is_eval: False + +video_cfg: + save_video: False + info_on_video: False + video_base_dir: ${runner.logger.log_path}/video/train diff --git a/examples/rlinf/config/plugrl_pendulum_ppo_mlp.yaml b/examples/rlinf/config/plugrl_pendulum_ppo_mlp.yaml new file mode 100644 index 0000000..81ab631 --- /dev/null +++ b/examples/rlinf/config/plugrl_pendulum_ppo_mlp.yaml @@ -0,0 +1,176 @@ +# RLinf's PPO with its MLP policy, on Pendulum run by the C++ client of +# plugrl-server's E44 (pendulum_client.cpp, no third-party libraries). +# +# plugrl_halfcheetah_ppo_mlp.yaml with rl-baselines3-zoo's published PPO +# settings for Pendulum-v1, as E44 used them: gamma 0.9, gae_lambda 0.95, +# 10 epochs, lr 1e-3, clip 0.2, no entropy bonus, minibatch 64 (rl-zoo leaves +# batch_size at SB3's default). (E44's pilot found that defaults barely learn +# Pendulum.) 16 envs x 256 steps = 4,096 frames per epoch; 25 epochs is E44's +# 102,400 steps. +# +# Copy this file to RLinf's examples/embodiment/config/ and env/plugrl_pendulum.yaml +# to examples/embodiment/config/env/. +defaults: + - env/plugrl_pendulum@env.train + - env/plugrl_pendulum@env.eval + - model/mlp_policy@actor.model + - hybrid_engines/fsdp@actor.fsdp_config + - weight_syncer/patch_syncer@weight_syncer + - override hydra/job_logging: stdout + - _self_ + +hydra: + run: + dir: . + output_subdir: null + +cluster: + num_nodes: 1 + component_placement: + actor,env,rollout: 0 + +runner: + task_type: embodied + logger: + log_path: "../results" + project_name: rlinf + experiment_name: "plugrl_pendulum_ppo_mlp" + logger_backends: ["tensorboard"] + + max_epochs: 25 + max_steps: -1 + + only_eval: False + val_check_interval: -1 + save_interval: 20 + + seq_length: 1 + max_prompt_length: 1 + + resume_dir: null + ckpt_path: null + +algorithm: + update_epoch: 10 + normalize_advantages: True + kl_penalty: kl + group_size: 1 + + reward_type: action_level + logprob_type: action_level + entropy_type: action_level + + adv_type: gae + loss_type: actor_critic + loss_agg_func: token-mean + kl_beta: 0.0 + entropy_bonus: 0.0 + + clip_ratio_high: 0.2 + clip_ratio_low: 0.2 + clip_ratio_c: 3.0 + value_clip: 1.0 + huber_delta: 10.0 + + gamma: 0.9 + gae_lambda: 0.95 + + reward_coef: 1.0 + +env: + group_name: EnvGroup + + train: + rollout_epoch: 1 + total_num_envs: 16 + group_size: ${algorithm.group_size} + max_steps_per_rollout_epoch: 256 + max_episode_steps: 200 + reward_coef: ${algorithm.reward_coef} + is_eval: False + + eval: + rollout_epoch: 1 + total_num_envs: 16 + group_size: ${algorithm.group_size} + max_steps_per_rollout_epoch: 256 + max_episode_steps: 200 + reward_coef: ${algorithm.reward_coef} + is_eval: True + +rollout: + group_name: RolloutGroup + backend: huggingface + enable_offload: false + pipeline_stage_num: 1 + + model: + model_path: "" + precision: ${actor.model.precision} + + enable_torch_compile: False + enable_cuda_graph: False + +actor: + group_name: ActorGroup + training_backend: fsdp + # rl-zoo's Pendulum-v1 PPO leaves batch_size at SB3's default, 64: with + # 10 epochs, 640 updates per 4,096-frame rollout. The 1,024 this config + # first inherited from RLinf's CartPole template made 40 (E49's pilot). + micro_batch_size: 64 + global_batch_size: 64 + seed: 1234 + enable_offload: false + + model: + model_type: mlp_policy + model_path: "" + obs_dim: 3 + action_dim: 1 + num_action_chunks: 1 + hidden_dim: 256 + precision: "32" + add_value_head: true + is_lora: false + lora_rank: 32 + + optim: + lr: 1.0e-3 + value_lr: 1.0e-3 + adam_beta1: 0.9 + adam_beta2: 0.999 + adam_eps: 1.0e-08 + weight_decay: 0.0 + clip_grad: 0.5 + + fsdp_config: + strategy: fsdp + sharding_strategy: no_shard + gradient_checkpointing: false + cpu_offload: false + offload_pin_memory: false + reshard_after_forward: true + enable_gradient_accumulation: true + forward_prefetch: false + limit_all_gathers: false + backward_prefetch: null + use_orig_params: false + use_liger_kernel: false + + mixed_precision: + param_dtype: ${actor.model.precision} + reduce_dtype: ${actor.model.precision} + buffer_dtype: ${actor.model.precision} + + amp_autocast: + enabled: false + precision: "bf16" + + grad_scaler: + enabled: false + +reward: + use_reward_model: false + +critic: + use_critic_model: false diff --git a/examples/rlinf/cross_clients.py b/examples/rlinf/cross_clients.py index 8416fa0..f289325 100644 --- a/examples/rlinf/cross_clients.py +++ b/examples/rlinf/cross_clients.py @@ -30,6 +30,14 @@ def main() -> int: p.add_argument("--seed", type=int, required=True) p.add_argument("--out", required=True) a = p.parse_args() + if not a.run_dir.startswith("/"): + # Git Bash rewrites /home/... into D:/Program Files/Git/home/... when it + # starts a Windows program. The poll below would then never find the + # run's end, and the clients would outlive it (E48's AMENDMENT.md). + p.error( + f"--run-dir {a.run_dir!r} is not a path on the remote machine; " + "from Git Bash, run with MSYS_NO_PATHCONV=1" + ) out = pathlib.Path(a.out) out.mkdir(parents=True, exist_ok=True) diff --git a/examples/rlinf/install_guangzhao.sh b/examples/rlinf/install_guangzhao.sh new file mode 100755 index 0000000..9cf1a32 --- /dev/null +++ b/examples/rlinf/install_guangzhao.sh @@ -0,0 +1,33 @@ +#!/usr/bin/env bash +# Point an RLinf checkout at plugrl-bridges: install it into RLinf's venv, +# patch env_type plugrl in (or move an older patch that imported +# plugrl_rlinf over to plugrl_bridges.rlinf), copy the example configs, and +# check that RLinf resolves the environment class. +# +# bash install_guangzhao.sh [RLINF_DIR] [BRIDGES_DIR] +set -euo pipefail +export PATH="$HOME/.local/bin:$PATH" +BASE="$HOME/zuogou/plugrl" +RLINF="${1:-$BASE/rlinf-src}" +BRIDGES="${2:-$BASE/plugrl-bridges}" +PY="$RLINF/.venv/bin/python" +F="$RLINF/rlinf/envs/__init__.py" + +if grep -q "plugrl_bridges.rlinf" "$F"; then + echo "already patched for plugrl-bridges" +elif grep -q "from plugrl_rlinf.env import PlugRLEnv" "$F"; then + sed -i 's/from plugrl_rlinf.env import PlugRLEnv/from plugrl_bridges.rlinf import PlugRLEnv/' "$F" + echo "moved the plugrl_rlinf patch over to plugrl_bridges.rlinf" +else + (cd "$RLINF" && patch -p1 < "$BRIDGES/patches/rlinf-plugrl-env-type.patch") +fi +uv pip install --python "$PY" -e "$BRIDGES" +cp "$BRIDGES"/examples/rlinf/config/plugrl_*_ppo_mlp.yaml "$RLINF/examples/embodiment/config/" +cp "$BRIDGES"/examples/rlinf/config/env/plugrl_*.yaml "$RLINF/examples/embodiment/config/env/" +# RLinf is not installed as a package; its launcher puts the checkout on +# PYTHONPATH, so the check does too. +(cd "$RLINF" && PYTHONPATH="$RLINF" "$PY" - <<'EOF' +from rlinf.envs import SupportedEnvType, get_env_cls +print("env cls", get_env_cls(SupportedEnvType("plugrl").value)) +EOF +) diff --git a/examples/rlinf/kill_orphans.sh b/examples/rlinf/kill_orphans.sh old mode 100644 new mode 100755 diff --git a/examples/rlinf/run_guangzhao.sh b/examples/rlinf/run_guangzhao.sh old mode 100644 new mode 100755 diff --git a/examples/run_bridged.sh b/examples/run_bridged.sh new file mode 100755 index 0000000..608dbc5 --- /dev/null +++ b/examples/run_bridged.sh @@ -0,0 +1,92 @@ +#!/usr/bin/env bash +# One bridged training run on guangzhao: a trainer that was not written for +# PlugRL, and 16 env clients on the same machine, one env each. +# +# bash run_bridged.sh TRAINER ENV OUT_DIR SEED [STEPS] +# +# TRAINER rlinf | sb3 | cleanrl +# ENV halfcheetah plugrl-env-client's mujoco-v1 (Python), 16 processes +# pendulum plugrl-server E44's pendulum_client (C++, no +# third-party libraries), 16 processes +# STEPS total environment steps; defaults: 409,600 (halfcheetah), 102,400 +# (pendulum), E43's and E44's budgets +# +# Paths are guangzhao's, set below. The clients start in a process group of +# their own and are stopped with it, whatever the trainer does at its end. +# Whatever the trainer, the bridge writes every episode's return to +# OUT_DIR/episodes.csv. +set -uo pipefail +TRAINER="$1" +ENV="$2" +OUT="$3" +SEED="$4" +case "$OUT" in /*) ;; *) OUT="$(pwd)/$OUT" ;; esac +BASE="$HOME/zuogou/plugrl" +BRIDGES="$BASE/plugrl-bridges" +RLINF="$BASE/rlinf-src" +TRAIN_PY="$BASE/bridges-venv/bin/python" # sb3, cleanrl: torch (CPU), SB3, tyro +CLIENT_PY="$BASE/plugrl-env-client/.venv/bin/python" +CLIENT_PATH="$BASE/e46/client-src:$BASE/e46/protocol-src" +CPP_CLIENT="$BASE/e44-build/pendulum_client" +PORT="${PORT:-8810}" +export PATH="$HOME/.local/bin:$PATH" OMP_NUM_THREADS=1 +export PLUGRL_BRIDGES_EPISODE_LOG="$OUT/episodes.csv" +mkdir -p "$OUT" + +case "$ENV" in + halfcheetah) STEPS="${5:-409600}"; ACTION_DIM=6; LOW=-1; HIGH=1 ;; + pendulum) STEPS="${5:-102400}"; ACTION_DIM=1; LOW=-2; HIGH=2 ;; + *) echo "ENV must be halfcheetah or pendulum" >&2; exit 2 ;; +esac +EPOCHS=$((STEPS / 4096)) # 16 envs x 256 steps a rollout, for every trainer + +if pgrep -u "$USER" -f "[p]lugrl-env-client/.venv/bin/python|[p]endulum_client " >/dev/null; then + echo "env clients are still running here; stop them first" >&2 + exit 1 +fi +{ date '+start %F %T'; echo "trainer $TRAINER env $ENV seed $SEED steps $STEPS port $PORT"; } | tee "$OUT/run.txt" + +case "$TRAINER" in + rlinf) + cfg="plugrl_${ENV}_ppo_mlp" + (cd "$RLINF" && source .venv/bin/activate && MUJOCO_GL=egl PYOPENGL_PLATFORM=egl PYTHONPATH="$RLINF" \ + exec setsid python examples/embodiment/train_embodied_agent.py \ + --config-path "$RLINF/examples/embodiment/config/" --config-name "$cfg" \ + runner.logger.log_path="$OUT/trainer-logs" actor.seed="$SEED" runner.max_epochs="$EPOCHS" \ + env.train.port="$PORT" env.train.episode_log="$PLUGRL_BRIDGES_EPISODE_LOG" \ + > "$OUT/trainer.log" 2>&1) & ;; + sb3) + preset=default; [ "$ENV" = pendulum ] && preset=pendulum + (cd "$OUT" && exec setsid "$TRAIN_PY" "$BRIDGES/examples/sb3/ppo.py" --num-envs 16 --port "$PORT" \ + --action-dim "$ACTION_DIM" --action-low "$LOW" --action-high "$HIGH" \ + --total-timesteps "$STEPS" --preset "$preset" --seed "$SEED" --log-dir "$OUT/trainer-logs" \ + > "$OUT/trainer.log" 2>&1) & ;; + cleanrl) + (cd "$OUT" && exec setsid "$TRAIN_PY" "$BRIDGES/examples/cleanrl/ppo_continuous_action.py" \ + --num-envs 16 --num-steps 256 --total-timesteps "$STEPS" --port "$PORT" \ + --action-dim "$ACTION_DIM" --action-low "$LOW" --action-high "$HIGH" --seed "$SEED" --no-cuda \ + > "$OUT/trainer.log" 2>&1) & ;; + *) echo "TRAINER must be rlinf, sb3 or cleanrl" >&2; exit 2 ;; +esac +TRAINER_PID=$! + +if [ "$ENV" = halfcheetah ]; then + (cd "$OUT" && PYTHONPATH="$CLIENT_PATH" exec setsid "$CLIENT_PY" -m plugrl_env_client.cli mujoco-v1 \ + --server-host 127.0.0.1 --server-port "$PORT" --num-procs 16 --num-envs 1 \ + --num-episodes 1000000 --runner.replan-steps 1 --runner.seed "$SEED" \ + --exp-name clients > "$OUT/clients.log" 2>&1) & +else + # 16 C++ processes, seeds SEED*100+1 ... +16, one process group. + (cd "$OUT" && exec setsid bash -c "for i in \$(seq 16); do '$CPP_CLIENT' 127.0.0.1 $PORT \$(( $SEED * 100 + i )) > cpp-client-\$i.log 2>&1 & done; wait") & +fi +CLIENT_PID=$! + +wait "$TRAINER_PID" +echo "trainer rc=$? at $(date '+%T')" | tee -a "$OUT/run.txt" +grep -E "Traceback|ClientLost|TimeoutError" "$OUT/trainer.log" | head -3 | tee -a "$OUT/run.txt" +sleep 5 +kill -- "-$CLIENT_PID" 2>/dev/null +wait "$CLIENT_PID" 2>/dev/null +echo "clients stopped" | tee -a "$OUT/run.txt" +[ "$TRAINER" = rlinf ] && (cd "$RLINF" && .venv/bin/ray stop --force >/dev/null 2>&1) +{ date '+end %F %T'; echo RUN_DONE; } | tee -a "$OUT/run.txt" diff --git a/examples/sb3/ppo.py b/examples/sb3/ppo.py new file mode 100644 index 0000000..0df6938 --- /dev/null +++ b/examples/sb3/ppo.py @@ -0,0 +1,98 @@ +"""Stable-Baselines3's PPO trains PlugRL env clients through the bridge. + + python ppo.py --num-envs 16 --port 8800 --action-dim 6 \ + --total-timesteps 409600 --seed 0 --log-dir runs/sb3-seed0 + +Start the env clients against --port: one env each, one-step chunks, for +example `plugrl-run-env-client mujoco-v1 --server-port 8800 --num-procs 16 +--num-envs 1 --runner.replan-steps 1`. + +By default PPO runs with SB3's defaults except `n_steps` (256 per env, so +that a rollout is 16 x 256 = 4,096 frames), behind VecNormalize as SB3's +MuJoCo examples are. `--preset pendulum` uses rl-baselines3-zoo's published +PPO settings for Pendulum-v1 instead (gamma 0.9, gae_lambda 0.95, 10 +epochs, lr 1e-3, clip 0.2, gSDE every 4 steps, no normalisation). VecMonitor +writes every episode's raw return and length to /monitor.csv. +""" + +from __future__ import annotations + +import argparse +import pathlib + +import gymnasium as gym +import numpy as np +from stable_baselines3 import PPO +from stable_baselines3.common.vec_env import VecMonitor, VecNormalize + +from plugrl_bridges.sb3 import PlugRLVecEnv + +# rl-baselines3-zoo hyperparams/ppo.yml, Pendulum-v1 (n_envs and n_steps +# are this script's: 16 x 256 per rollout, where rl-zoo uses 4 x 1,024). +PRESETS = { + "default": dict(normalize=True, ppo={}), + "pendulum": dict( + normalize=False, + ppo=dict( + gamma=0.9, + gae_lambda=0.95, + n_epochs=10, + ent_coef=0.0, + learning_rate=1e-3, + clip_range=0.2, + use_sde=True, + sde_sample_freq=4, + ), + ), +} + + +def main() -> int: + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--num-envs", type=int, default=16) + p.add_argument("--port", type=int, default=8800) + p.add_argument("--host", default="0.0.0.0") + p.add_argument("--action-dim", type=int, default=6) + p.add_argument("--action-low", type=float, default=-1.0) + p.add_argument("--action-high", type=float, default=1.0) + p.add_argument("--state-keys", nargs="+", default=["obs"]) + p.add_argument("--total-timesteps", type=int, default=409_600) + p.add_argument("--n-steps", type=int, default=256) + p.add_argument("--preset", choices=sorted(PRESETS), default="default") + p.add_argument("--seed", type=int, default=0) + p.add_argument("--log-dir", default="runs/sb3") + a = p.parse_args() + + preset = PRESETS[a.preset] + log_dir = pathlib.Path(a.log_dir) + log_dir.mkdir(parents=True, exist_ok=True) + env = PlugRLVecEnv( + num_envs=a.num_envs, + action_space=gym.spaces.Box( + a.action_low, a.action_high, (a.action_dim,), np.float32 + ), + port=a.port, + host=a.host, + state_keys=tuple(a.state_keys), + ) + env = VecMonitor(env, filename=str(log_dir / "monitor.csv")) + if preset["normalize"]: + env = VecNormalize(env, gamma=preset["ppo"].get("gamma", 0.99)) + model = PPO( + "MlpPolicy", + env, + n_steps=a.n_steps, + seed=a.seed, + tensorboard_log=str(log_dir), + device="cpu", + verbose=1, + **preset["ppo"], + ) + model.learn(total_timesteps=a.total_timesteps) + model.save(log_dir / "model") + env.close() + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/examples/smoke_probe.py b/examples/smoke_probe.py new file mode 100644 index 0000000..982a41b --- /dev/null +++ b/examples/smoke_probe.py @@ -0,0 +1,79 @@ +"""Smoke test: a trainer's example trains two real env clients on the probe env, briefly. + + python smoke_probe.py --trainer cleanrl|sb3 --client-python PATH/TO/env-client/python + +Not a learning test: it checks that the trainer runs end to end on the +bridge with real clients, logs their episodes, and that the clients exit +when it closes. +""" + +from __future__ import annotations + +import argparse +import pathlib +import socket +import subprocess +import sys +import tempfile + +HERE = pathlib.Path(__file__).resolve().parent + + +def free_port() -> int: + with socket.socket() as s: + s.bind(("127.0.0.1", 0)) + return s.getsockname()[1] + + +def trainer_command(trainer: str, port: int, log_dir: str) -> list[str]: + common = ["--port", str(port), "--host", "127.0.0.1", "--action-dim", "3"] + if trainer == "cleanrl": + return [sys.executable, str(HERE / "cleanrl" / "ppo_continuous_action.py"), + "--num-envs", "2", "--num-steps", "8", "--num-minibatches", "2", + "--update-epochs", "1", "--total-timesteps", "64", "--no-cuda", + "--state-keys", "t", "a", *common] # fmt: skip + return [sys.executable, str(HERE / "sb3" / "ppo.py"), "--num-envs", "2", + "--n-steps", "16", "--total-timesteps", "64", "--state-keys", "t", "a", + "--log-dir", log_dir, *common] # fmt: skip + + +def main() -> int: + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--trainer", choices=("cleanrl", "sb3"), required=True) + p.add_argument("--client-python", required=True) + a = p.parse_args() + port = free_port() + log_dir = tempfile.mkdtemp(prefix="plugrl-smoke-") + trainer = subprocess.Popen( + trainer_command(a.trainer, port, log_dir), + cwd=log_dir, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, + ) # fmt: skip + client = subprocess.Popen( + [a.client_python, "-m", "plugrl_env_client.cli", "probe-v1", + "--server-host", "127.0.0.1", "--server-port", str(port), + "--num-envs", "2", "--num-episodes", "1000000", "--runner.replan-steps", "1"], + cwd=log_dir, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, + ) # fmt: skip + out, _ = trainer.communicate(timeout=300) + try: + code = client.wait(timeout=60) + except subprocess.TimeoutExpired: + client.kill() + code = "killed" + if a.trainer == "cleanrl": + episodes = sum("episodic_return" in line for line in out.splitlines()) + else: + monitor = pathlib.Path(log_dir) / "monitor.csv" + episodes = ( + max(0, len(monitor.read_text().splitlines()) - 2) if monitor.exists() else 0 + ) + print( + f"{a.trainer}: trainer exit {trainer.returncode}, client exit {code}, episodes logged {episodes}" + ) + if trainer.returncode != 0: + print("\n".join(out.splitlines()[-8:])) + return 0 if trainer.returncode == 0 and code == 0 and episodes else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/patches/rlinf-plugrl-env-type.patch b/patches/rlinf-plugrl-env-type.patch index b819b3e..13291b2 100644 --- a/patches/rlinf-plugrl-env-type.patch +++ b/patches/rlinf-plugrl-env-type.patch @@ -1,4 +1,4 @@ -RLinf at 034579c: add env_type plugrl (plugrl-rlinf's PlugRLEnv). +RLinf at 034579c: add env_type plugrl (plugrl-bridges' PlugRLEnv). Apply from the RLinf checkout: patch -p1 < rlinf-plugrl-env-type.patch --- a/rlinf/envs/__init__.py @@ -17,7 +17,7 @@ Apply from the RLinf checkout: patch -p1 < rlinf-plugrl-env-type.patch return PolarisEnv + elif env_type == SupportedEnvType.PLUGRL: -+ from plugrl_rlinf.env import PlugRLEnv ++ from plugrl_bridges.rlinf import PlugRLEnv + + return PlugRLEnv else: diff --git a/pyproject.toml b/pyproject.toml index be3abe4..f80eae1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [project] -name = "plugrl-rlinf" +name = "plugrl-bridges" version = "0.1.0" -description = "RLinf trains environments that PlugRL env clients run, through the PlugRL protocol" +description = "Trainers that were not written for PlugRL (RLinf, Stable-Baselines3, gymnasium) train PlugRL env clients" readme = "README.md" license = "Apache-2.0" requires-python = ">=3.10" @@ -10,18 +10,20 @@ authors = [ { name = "Zuo Gou" }, { name = "Zilin Kang" }, ] +# The bridge server needs only these; each adapter's trainer is an extra. dependencies = [ "numpy>=1.24", - "torch>=2.1", "websockets>=14", "plugrl-protocol @ git+https://github.com/PlugRL/plugrl-protocol.git@0a76192cc2456ee1136c5cbabe5206ca75e38f98", ] [project.optional-dependencies] -gym = ["gymnasium>=0.29"] +rlinf = ["torch>=2.1", "gymnasium>=0.29"] +gym = ["gymnasium>=1.0"] +sb3 = ["stable-baselines3>=2.3", "gymnasium>=1.0"] [dependency-groups] -dev = ["pytest>=8", "gymnasium>=0.29"] +dev = ["pytest>=8", "torch>=2.1", "gymnasium>=1.0", "stable-baselines3>=2.3"] [build-system] requires = ["hatchling"] @@ -31,10 +33,23 @@ build-backend = "hatchling.build" allow-direct-references = true [tool.hatch.build.targets.wheel] -packages = ["src/plugrl_rlinf"] +packages = ["src/plugrl_bridges"] [tool.pytest.ini_options] testpaths = ["tests"] [tool.ruff] line-length = 88 + +# CleanRL's script stays line-for-line comparable with upstream. +[tool.ruff.format] +exclude = ["examples/cleanrl/*"] + +# CI and local development take torch's CPU build; RLinf brings its own. +[[tool.uv.index]] +name = "pytorch-cpu" +url = "https://download.pytorch.org/whl/cpu" +explicit = true + +[tool.uv.sources] +torch = { index = "pytorch-cpu" } diff --git a/src/plugrl_bridges/__init__.py b/src/plugrl_bridges/__init__.py new file mode 100644 index 0000000..d2aed24 --- /dev/null +++ b/src/plugrl_bridges/__init__.py @@ -0,0 +1,17 @@ +"""Trainers that were not written for PlugRL train environments that PlugRL env clients run. + +One protocol server, `BridgeServer`, steps every connected env client in +lockstep; an adapter per trainer puts it behind that trainer's own +environment interface: + +- `plugrl_bridges.rlinf.PlugRLEnv`: RLinf's environment contract (env_type plugrl). +- `plugrl_bridges.gym.PlugRLVectorEnv`: a gymnasium VectorEnv (CleanRL, TorchRL, ...). +- `plugrl_bridges.sb3.PlugRLVecEnv`: a Stable-Baselines3 VecEnv. + +The adapters import their trainer's package; the server needs none of them. +""" + +from plugrl_bridges.server import BridgeServer, ClientLost, SlotResult + +__all__ = ["BridgeServer", "ClientLost", "SlotResult"] +__version__ = "0.1.0" diff --git a/src/plugrl_bridges/gym.py b/src/plugrl_bridges/gym.py new file mode 100644 index 0000000..c78884d --- /dev/null +++ b/src/plugrl_bridges/gym.py @@ -0,0 +1,170 @@ +"""A gymnasium VectorEnv whose environments are PlugRL env clients. + +Any trainer that takes a `gymnasium.vector.VectorEnv` - CleanRL's scripts, +TorchRL, a loop of your own - can train environments that run in other +processes, on other machines or in other languages, through the PlugRL +protocol: + + env = PlugRLVectorEnv(num_envs=16, port=8800, + action_space=gym.spaces.Box(-1, 1, (6,), np.float32)) + obs, _ = env.reset() + obs, rewards, terminated, truncated, infos = env.step(actions) + +Construction starts a BridgeServer and blocks until `num_envs` environments +have connected and asked for their first action; the observation space is +read off what they sent. The clients run one-step chunks (`action_horizon` +1), and reset their own environments when an episode ends. + +Autoreset is gymnasium's SAME_STEP mode: the step that ends an env's +episode returns that env's reset observation, and the terminal one is in +`infos["final_obs"]`, masked by `infos["_final_obs"]`. `reset()` cannot end +the episodes in progress - the protocol has no way for a server to ask - so +it returns the observations the next step starts from. + +Episode statistics come with it, in `infos["episode"]` ({"r", "l", "t"}, +masked by `infos["_episode"]`), counted from the raw rewards. Do not wrap it +in gymnasium's `wrappers.vector.RecordEpisodeStatistics`: as of gymnasium +1.3 that wrapper counts as if autoreset were NEXT_STEP, and here it would +drop the first reward of every episode. +""" + +from __future__ import annotations + +import time +from typing import Any + +import gymnasium as gym +import numpy as np +from gymnasium.vector import AutoresetMode +from gymnasium.vector.utils import batch_space + +from plugrl_bridges.observations import EpisodeStats, stack_states +from plugrl_bridges.server import BridgeServer + + +class PlugRLVectorEnv(gym.vector.VectorEnv): + metadata = {"autoreset_mode": AutoresetMode.SAME_STEP} + + def __init__( + self, + *, + num_envs: int, + action_space: gym.spaces.Box, + port: int = 8800, + host: str = "0.0.0.0", + state_keys: tuple[str, ...] = ("obs",), + timeout: float = 600.0, + episode_log: str | None = None, + ) -> None: + self.num_envs = int(num_envs) + self.state_keys = list(state_keys) + self.single_action_space = action_space + self.action_space = batch_space(action_space, self.num_envs) + self.server = BridgeServer( + host=host, + port=port, + num_envs=self.num_envs, + action_horizon=1, + action_dim=int(np.prod(action_space.shape)), + timeout=timeout, + episode_log=episode_log, + ) + self.server.start() + self._next = stack_states(self.server.gather(), self.state_keys) + self.single_observation_space = gym.spaces.Box( + -np.inf, np.inf, (self._next.shape[1],), np.float32 + ) + self.observation_space = batch_space( + self.single_observation_space, self.num_envs + ) + self._stats = EpisodeStats(self.num_envs) + self._started = np.full(self.num_envs, time.perf_counter()) + + def reset(self, *, seed: Any = None, options: dict | None = None): + # The clients seed and reset their own environments. + return self._next.copy(), {} + + def step(self, actions): + actions = np.asarray(actions, dtype=np.float32).reshape(self.num_envs, 1, -1) + results = self.server.step(actions) + obs = stack_states([r.next_obs for r in results], self.state_keys) + rewards = np.asarray([r.reward for r in results], dtype=np.float64) + terminated = np.asarray([r.terminated for r in results], dtype=np.bool_) + truncated = np.asarray([r.truncated for r in results], dtype=np.bool_) + done = terminated | truncated + self._stats.add(rewards) + infos: dict[str, Any] = {} + if done.any(): + now = time.perf_counter() + infos["episode"] = { + "r": np.where(done, self._stats.returns, 0.0), + "l": np.where(done, self._stats.lengths, 0), + "t": np.where(done, np.round(now - self._started, 6), 0.0), + } + infos["_episode"] = done.copy() + self._started[done] = now + self._stats.end(done) + final_obs = np.full(self.num_envs, None, dtype=object) + for i in np.flatnonzero(done): + final_obs[i] = stack_states([results[i].feedback_obs], self.state_keys)[ + 0 + ] + infos["final_obs"] = final_obs + infos["_final_obs"] = done.copy() + infos["final_info"] = {} + infos["_final_info"] = done.copy() + self._next = obs + return obs, rewards, terminated, truncated, infos + + def close_extras(self, **kwargs: Any) -> None: + self.server.close() + + +class NormalizeObservation(gym.vector.VectorWrapper): + """gymnasium's vector NormalizeObservation (and an optional clip), for SAME_STEP. + + gymnasium's own vector observation wrappers refuse SAME_STEP autoreset, + because they would leave `infos["final_obs"]` untransformed. This one + normalizes the observations and the final ones with the same running + mean and variance, updated from each batch of observations as + gymnasium's is. + """ + + def __init__( + self, + env: gym.vector.VectorEnv, + epsilon: float = 1e-8, + clip: float | None = None, + ) -> None: + super().__init__(env) + from gymnasium.wrappers.utils import RunningMeanStd + + self.obs_rms = RunningMeanStd( + shape=env.single_observation_space.shape, dtype=np.float64 + ) + self.epsilon = epsilon + self.clip = clip + self.update_running_mean = True + + def _normalize(self, obs: np.ndarray) -> np.ndarray: + out = (obs - self.obs_rms.mean) / np.sqrt(self.obs_rms.var + self.epsilon) + if self.clip is not None: + out = np.clip(out, -self.clip, self.clip) + return out.astype(np.float32) + + def reset(self, *, seed: Any = None, options: dict | None = None): + obs, infos = self.env.reset(seed=seed, options=options) + if self.update_running_mean: + self.obs_rms.update(obs) + return self._normalize(obs), infos + + def step(self, actions): + obs, rewards, terminated, truncated, infos = self.env.step(actions) + if self.update_running_mean: + self.obs_rms.update(obs) + if "final_obs" in infos: + final = infos["final_obs"].copy() + for i in np.flatnonzero(infos["_final_obs"]): + final[i] = self._normalize(final[i][None])[0] + infos = {**infos, "final_obs": final} + return self._normalize(obs), rewards, terminated, truncated, infos diff --git a/src/plugrl_bridges/observations.py b/src/plugrl_bridges/observations.py new file mode 100644 index 0000000..3df8305 --- /dev/null +++ b/src/plugrl_bridges/observations.py @@ -0,0 +1,43 @@ +"""PlugRL observations as the flat state vectors trainers expect.""" + +from __future__ import annotations + +import numpy as np + + +def stack_states(rows: list[dict], state_keys: list[str]) -> np.ndarray: + """float32 [n, D]: each row's `state_keys`, flattened and concatenated in order.""" + parts = [] + for row in rows: + states = row["states"] + missing = [k for k in state_keys if k not in states] + if missing: + raise KeyError( + f"a client sent states {sorted(states)}; state_keys asks for {missing}" + ) + parts.append( + np.concatenate( + [ + np.asarray(states[k], dtype=np.float32).reshape(-1) + for k in state_keys + ] + ) + ) + return np.stack(parts) + + +class EpisodeStats: + """Each env's return and length so far: the bridge counts them itself, so + that a trainer's episode logging does not depend on what a client sends.""" + + def __init__(self, n: int) -> None: + self.returns = np.zeros(n, dtype=np.float64) + self.lengths = np.zeros(n, dtype=np.int64) + + def add(self, rewards: np.ndarray, steps: int = 1) -> None: + self.returns += rewards + self.lengths += steps + + def end(self, done: np.ndarray) -> None: + self.returns[done] = 0.0 + self.lengths[done] = 0 diff --git a/src/plugrl_rlinf/env.py b/src/plugrl_bridges/rlinf.py similarity index 92% rename from src/plugrl_rlinf/env.py rename to src/plugrl_bridges/rlinf.py index 52406e2..cfc7826 100644 --- a/src/plugrl_rlinf/env.py +++ b/src/plugrl_bridges/rlinf.py @@ -1,247 +1,236 @@ -"""The RLinf side of the bridge: an RLinf environment whose environments are PlugRL env clients. - -RLinf's environment worker constructs an environment class and calls -`reset()` once, then `chunk_step(actions)` for every step (with -`auto_reset: True`). `PlugRLEnv` implements that contract on top of a -BridgeServer. Every slot is an environment that a PlugRL env client runs, -anywhere that can reach this port, in any language that speaks the protocol. - -What RLinf gets matches its own simulators under auto-reset: -- the observation after the step, which for an env whose episode ended is - the reset observation the client sent with its next infer; -- `infos["final_observation"]`, where those envs carry the terminal - observation from the feedback, with `_final_observation` as the mask; -- `infos["final_info"]["episode"]`: the return, the length and the mean - reward of each episode that ended; -- `infos["episode"]`: the same, for the episodes in progress. - -Observations become `{"states": float32 [N, D]}`: the `state_keys` of the -client's `states`, flattened and concatenated in that order. With -`image_key`, that image is added as `main_images` (uint8 [N, H, W, C]), and -the text as `task_descriptions`. - -Configuration, under `env.train` in an RLinf config: - - env_type: plugrl - port: 8800 # this process listens on port + seed_offset - host: 0.0.0.0 - state_keys: [obs] - image_key: null - action_dim: 6 - action_low: -1.0 # the action space RLinf samples from - action_high: 1.0 - timeout: 600 # seconds to wait for every client, per step - auto_reset: True # required: a PlugRL client resets itself -""" - -from __future__ import annotations - -from typing import Any - -import numpy as np -import torch - -from plugrl_rlinf.server import BridgeServer, SlotResult - -try: - import gymnasium as gym - - _Base = gym.Env -except ImportError: # the bridge itself does not need it; RLinf's wrappers do - gym = None - _Base = object - - -def _get(cfg: Any, key: str, default: Any = None) -> Any: - if cfg is None: - return default - if isinstance(cfg, dict): - return cfg.get(key, default) - getter = getattr(cfg, "get", None) - if callable(getter): - return getter(key, default) - return getattr(cfg, key, default) - - -class PlugRLEnv(_Base): - metadata = {"render_modes": []} - - def __init__( - self, - cfg: Any, - num_envs: int, - seed_offset: int, - total_num_processes: int, - worker_info: Any = None, - ) -> None: - if not bool(_get(cfg, "auto_reset", True)): - raise ValueError( - "env_type plugrl needs auto_reset: True. A PlugRL env client resets " - "its own environments; the protocol has no way for a server to ask." - ) - self.cfg = cfg - self.num_envs = int(num_envs) - self.seed_offset = int(seed_offset) - self.total_num_processes = int(total_num_processes) - self.worker_info = worker_info - self.state_keys = list(_get(cfg, "state_keys", ["obs"])) - self.image_key = _get(cfg, "image_key", None) - self.action_dim = int(_get(cfg, "action_dim")) - self.action_horizon = int(_get(cfg, "action_horizon", 1)) - low = np.full(self.action_dim, float(_get(cfg, "action_low", -1.0)), np.float32) - high = np.full( - self.action_dim, float(_get(cfg, "action_high", 1.0)), np.float32 - ) - if gym is not None: - self.action_space = gym.spaces.Box(low=low, high=high, dtype=np.float32) - self._low, self._high = low, high - self.port = int(_get(cfg, "port", 8800)) + self.seed_offset - self.server = BridgeServer( - host=str(_get(cfg, "host", "0.0.0.0")), - port=self.port, - num_envs=self.num_envs, - action_horizon=self.action_horizon, - action_dim=self.action_dim, - timeout=float(_get(cfg, "timeout", 600.0)), - ) - self.server.start() - self._device = torch.device("cpu") - self._is_start = True - self._elapsed_steps = torch.zeros(self.num_envs, dtype=torch.int32) - self._returns = torch.zeros(self.num_envs, dtype=torch.float32) - - # ------------------------------------------------- the attributes RLinf reads - - @property - def device(self) -> torch.device: - return self._device - - @property - def elapsed_steps(self) -> torch.Tensor: - return self._elapsed_steps - - @property - def is_start(self) -> bool: - return self._is_start - - @is_start.setter - def is_start(self, value: bool) -> None: - self._is_start = value - - @property - def info_logging_keys(self) -> list[str]: - return [] - - # ------------------------------------------------------- observations - - def _wrap(self, rows: list[dict]) -> dict[str, Any]: - parts = [] - for row in rows: - states = row["states"] - missing = [k for k in self.state_keys if k not in states] - if missing: - raise KeyError( - f"a client sent states {sorted(states)}; state_keys asks for {missing}" - ) - parts.append( - np.concatenate( - [ - np.asarray(states[k], dtype=np.float32).reshape(-1) - for k in self.state_keys - ] - ) - ) - obs: dict[str, Any] = {"states": torch.from_numpy(np.stack(parts))} - if self.image_key: - obs["main_images"] = torch.from_numpy( - np.stack([np.asarray(row["images"][self.image_key]) for row in rows]) - ) - obs["task_descriptions"] = [row["text"] for row in rows] - return obs - - def _episode(self) -> dict[str, torch.Tensor]: - length = self._elapsed_steps.clone() - ret = self._returns.clone() - return { - "return": ret, - "episode_len": length, - "reward": ret / torch.clamp(length.float(), min=1.0), - } - - # ---------------------------------------------------------- the contract - - def reset(self, *, seed: Any = None, options: dict | None = None): - if options and options.get("env_idx") is not None: - raise NotImplementedError( - "a PlugRL env client resets its own environments; resetting " - "chosen ones from the trainer is not in the protocol" - ) - rows = self.server.gather() - self._elapsed_steps.zero_() - self._returns.zero_() - self._is_start = True - return self._wrap(rows), {} - - def chunk_step(self, chunk_actions): - if isinstance(chunk_actions, torch.Tensor): - chunk_actions = chunk_actions.detach().to("cpu", torch.float32).numpy() - chunk_actions = np.asarray(chunk_actions, dtype=np.float32) - if chunk_actions.ndim != 3: - raise ValueError( - "chunk_actions must be [num_envs, chunk_steps, action_dim], " - f"got {chunk_actions.shape}" - ) - results: list[SlotResult] = self.server.step(chunk_actions) - n, h = self.num_envs, chunk_actions.shape[1] - - rewards = torch.zeros((n, h), dtype=torch.float32) - terminations = torch.zeros((n, h), dtype=torch.bool) - truncations = torch.zeros((n, h), dtype=torch.bool) - # The client sums the chunk's rewards (SPEC.md section 5.4), so the - # sum goes on the chunk's last step: RLinf's chunk-level reward. - rewards[:, -1] = torch.tensor([r.reward for r in results]) - terminations[:, -1] = torch.tensor([r.terminated for r in results]) - truncations[:, -1] = torch.tensor([r.truncated for r in results]) - done = terminations[:, -1] | truncations[:, -1] - - self._elapsed_steps += h - self._returns += rewards[:, -1] - obs = self._wrap([r.next_obs for r in results]) - infos: dict[str, Any] = {"episode": self._episode()} - if done.any(): - final_obs = self._wrap( - [ - r.feedback_obs if d else r.next_obs - for r, d in zip(results, done.tolist()) - ] - ) - infos["final_observation"] = final_obs - infos["final_info"] = {"episode": self._episode()} - infos["_final_observation"] = done.clone() - infos["_final_info"] = done.clone() - infos["_elapsed_steps"] = done.clone() - self._elapsed_steps[done] = 0 - self._returns[done] = 0.0 - self._is_start = False - return [obs], rewards, terminations, truncations, [infos] - - def step(self, actions): - """One step of a one-step chunk, for callers that do not chunk.""" - actions = torch.as_tensor(actions, dtype=torch.float32).reshape( - self.num_envs, 1, -1 - ) - obs_list, rewards, terms, truncs, infos_list = self.chunk_step(actions) - return obs_list[-1], rewards[:, -1], terms[:, -1], truncs[:, -1], infos_list[-1] - - def update_reset_state_ids(self) -> None: - return None - - def sample_action_space(self) -> torch.Tensor: - return torch.from_numpy( - np.random.uniform( - self._low, self._high, (self.num_envs, self.action_dim) - ).astype(np.float32) - ) - - def close(self) -> None: - self.server.close() +"""The RLinf side of the bridge: an RLinf environment whose environments are PlugRL env clients. + +RLinf's environment worker constructs an environment class and calls +`reset()` once, then `chunk_step(actions)` for every step (with +`auto_reset: True`). `PlugRLEnv` implements that contract on top of a +BridgeServer. Every slot is an environment that a PlugRL env client runs, +anywhere that can reach this port, in any language that speaks the protocol. + +What RLinf gets matches its own simulators under auto-reset: +- the observation after the step, which for an env whose episode ended is + the reset observation the client sent with its next infer; +- `infos["final_observation"]`, where those envs carry the terminal + observation from the feedback, with `_final_observation` as the mask; +- `infos["final_info"]["episode"]`: the return, the length and the mean + reward of each episode that ended; +- `infos["episode"]`: the same, for the episodes in progress. + +Observations become `{"states": float32 [N, D]}`: the `state_keys` of the +client's `states`, flattened and concatenated in that order. With +`image_key`, that image is added as `main_images` (uint8 [N, H, W, C]), and +the text as `task_descriptions`. + +Configuration, under `env.train` in an RLinf config: + + env_type: plugrl + port: 8800 # this process listens on port + seed_offset + host: 0.0.0.0 + state_keys: [obs] + image_key: null + action_dim: 6 + action_low: -1.0 # the action space RLinf samples from + action_high: 1.0 + timeout: 600 # seconds to wait for every client, per step + auto_reset: True # required: a PlugRL client resets itself + episode_log: null # a CSV path: one line per episode (BridgeServer) +""" + +from __future__ import annotations + +from typing import Any + +import numpy as np +import torch + +from plugrl_bridges.observations import stack_states +from plugrl_bridges.server import BridgeServer, SlotResult + +try: + import gymnasium as gym + + _Base = gym.Env +except ImportError: # the bridge itself does not need it; RLinf's wrappers do + gym = None + _Base = object + + +def _get(cfg: Any, key: str, default: Any = None) -> Any: + if cfg is None: + return default + if isinstance(cfg, dict): + return cfg.get(key, default) + getter = getattr(cfg, "get", None) + if callable(getter): + return getter(key, default) + return getattr(cfg, key, default) + + +class PlugRLEnv(_Base): + metadata = {"render_modes": []} + + def __init__( + self, + cfg: Any, + num_envs: int, + seed_offset: int, + total_num_processes: int, + worker_info: Any = None, + ) -> None: + if not bool(_get(cfg, "auto_reset", True)): + raise ValueError( + "env_type plugrl needs auto_reset: True. A PlugRL env client resets " + "its own environments; the protocol has no way for a server to ask." + ) + self.cfg = cfg + self.num_envs = int(num_envs) + self.seed_offset = int(seed_offset) + self.total_num_processes = int(total_num_processes) + self.worker_info = worker_info + self.state_keys = list(_get(cfg, "state_keys", ["obs"])) + self.image_key = _get(cfg, "image_key", None) + self.action_dim = int(_get(cfg, "action_dim")) + self.action_horizon = int(_get(cfg, "action_horizon", 1)) + low = np.full(self.action_dim, float(_get(cfg, "action_low", -1.0)), np.float32) + high = np.full( + self.action_dim, float(_get(cfg, "action_high", 1.0)), np.float32 + ) + if gym is not None: + self.action_space = gym.spaces.Box(low=low, high=high, dtype=np.float32) + self._low, self._high = low, high + self.port = int(_get(cfg, "port", 8800)) + self.seed_offset + self.server = BridgeServer( + host=str(_get(cfg, "host", "0.0.0.0")), + port=self.port, + num_envs=self.num_envs, + action_horizon=self.action_horizon, + action_dim=self.action_dim, + timeout=float(_get(cfg, "timeout", 600.0)), + episode_log=_get(cfg, "episode_log", None), + ) + self.server.start() + self._device = torch.device("cpu") + self._is_start = True + self._elapsed_steps = torch.zeros(self.num_envs, dtype=torch.int32) + self._returns = torch.zeros(self.num_envs, dtype=torch.float32) + + # ------------------------------------------------- the attributes RLinf reads + + @property + def device(self) -> torch.device: + return self._device + + @property + def elapsed_steps(self) -> torch.Tensor: + return self._elapsed_steps + + @property + def is_start(self) -> bool: + return self._is_start + + @is_start.setter + def is_start(self, value: bool) -> None: + self._is_start = value + + @property + def info_logging_keys(self) -> list[str]: + return [] + + # ------------------------------------------------------- observations + + def _wrap(self, rows: list[dict]) -> dict[str, Any]: + obs: dict[str, Any] = { + "states": torch.from_numpy(stack_states(rows, self.state_keys)) + } + if self.image_key: + obs["main_images"] = torch.from_numpy( + np.stack([np.asarray(row["images"][self.image_key]) for row in rows]) + ) + obs["task_descriptions"] = [row["text"] for row in rows] + return obs + + def _episode(self) -> dict[str, torch.Tensor]: + length = self._elapsed_steps.clone() + ret = self._returns.clone() + return { + "return": ret, + "episode_len": length, + "reward": ret / torch.clamp(length.float(), min=1.0), + } + + # ---------------------------------------------------------- the contract + + def reset(self, *, seed: Any = None, options: dict | None = None): + if options and options.get("env_idx") is not None: + raise NotImplementedError( + "a PlugRL env client resets its own environments; resetting " + "chosen ones from the trainer is not in the protocol" + ) + rows = self.server.gather() + self._elapsed_steps.zero_() + self._returns.zero_() + self._is_start = True + return self._wrap(rows), {} + + def chunk_step(self, chunk_actions): + if isinstance(chunk_actions, torch.Tensor): + chunk_actions = chunk_actions.detach().to("cpu", torch.float32).numpy() + chunk_actions = np.asarray(chunk_actions, dtype=np.float32) + if chunk_actions.ndim != 3: + raise ValueError( + "chunk_actions must be [num_envs, chunk_steps, action_dim], " + f"got {chunk_actions.shape}" + ) + results: list[SlotResult] = self.server.step(chunk_actions) + n, h = self.num_envs, chunk_actions.shape[1] + + rewards = torch.zeros((n, h), dtype=torch.float32) + terminations = torch.zeros((n, h), dtype=torch.bool) + truncations = torch.zeros((n, h), dtype=torch.bool) + # The client sums the chunk's rewards (SPEC.md section 5.4), so the + # sum goes on the chunk's last step: RLinf's chunk-level reward. + rewards[:, -1] = torch.tensor([r.reward for r in results]) + terminations[:, -1] = torch.tensor([r.terminated for r in results]) + truncations[:, -1] = torch.tensor([r.truncated for r in results]) + done = terminations[:, -1] | truncations[:, -1] + + self._elapsed_steps += h + self._returns += rewards[:, -1] + obs = self._wrap([r.next_obs for r in results]) + infos: dict[str, Any] = {"episode": self._episode()} + if done.any(): + final_obs = self._wrap( + [ + r.feedback_obs if d else r.next_obs + for r, d in zip(results, done.tolist()) + ] + ) + infos["final_observation"] = final_obs + infos["final_info"] = {"episode": self._episode()} + infos["_final_observation"] = done.clone() + infos["_final_info"] = done.clone() + infos["_elapsed_steps"] = done.clone() + self._elapsed_steps[done] = 0 + self._returns[done] = 0.0 + self._is_start = False + return [obs], rewards, terminations, truncations, [infos] + + def step(self, actions): + """One step of a one-step chunk, for callers that do not chunk.""" + actions = torch.as_tensor(actions, dtype=torch.float32).reshape( + self.num_envs, 1, -1 + ) + obs_list, rewards, terms, truncs, infos_list = self.chunk_step(actions) + return obs_list[-1], rewards[:, -1], terms[:, -1], truncs[:, -1], infos_list[-1] + + def update_reset_state_ids(self) -> None: + return None + + def sample_action_space(self) -> torch.Tensor: + return torch.from_numpy( + np.random.uniform( + self._low, self._high, (self.num_envs, self.action_dim) + ).astype(np.float32) + ) + + def close(self) -> None: + self.server.close() diff --git a/src/plugrl_bridges/sb3.py b/src/plugrl_bridges/sb3.py new file mode 100644 index 0000000..285b926 --- /dev/null +++ b/src/plugrl_bridges/sb3.py @@ -0,0 +1,115 @@ +"""A Stable-Baselines3 VecEnv whose environments are PlugRL env clients. + + env = PlugRLVecEnv(num_envs=16, port=8800, + action_space=gym.spaces.Box(-1, 1, (6,), np.float32)) + PPO("MlpPolicy", env).learn(409_600) + +Construction starts a BridgeServer and blocks until `num_envs` environments +have connected and asked for their first action; the observation space is +read off what they sent. The clients run one-step chunks and reset their own +environments. + +What SB3 expects of an episode end is there: `terminal_observation` and +`TimeLimit.truncated` in that env's info, and `episode` (return, length, +time) as SB3's Monitor would put it, counted by the bridge, so SB3 logs +`rollout/ep_rew_mean` with no wrapper. `reset()` cannot end the episodes in +progress - the protocol has no way for a server to ask - so it returns the +observations the next step starts from. +""" + +from __future__ import annotations + +import time +from typing import Any + +import gymnasium as gym +import numpy as np +from stable_baselines3.common.vec_env.base_vec_env import VecEnv + +from plugrl_bridges.observations import EpisodeStats, stack_states +from plugrl_bridges.server import BridgeServer + + +class PlugRLVecEnv(VecEnv): + def __init__( + self, + *, + num_envs: int, + action_space: gym.spaces.Box, + port: int = 8800, + host: str = "0.0.0.0", + state_keys: tuple[str, ...] = ("obs",), + timeout: float = 600.0, + episode_log: str | None = None, + ) -> None: + self.state_keys = list(state_keys) + self.server = BridgeServer( + host=host, + port=port, + num_envs=num_envs, + action_horizon=1, + action_dim=int(np.prod(action_space.shape)), + timeout=timeout, + episode_log=episode_log, + ) + self.server.start() + self._next = stack_states(self.server.gather(), self.state_keys) + observation_space = gym.spaces.Box( + -np.inf, np.inf, (self._next.shape[1],), np.float32 + ) + super().__init__(num_envs, observation_space, action_space) + self._stats = EpisodeStats(num_envs) + self._started = time.time() + self._actions: np.ndarray | None = None + + def reset(self) -> np.ndarray: + # The clients seed and reset their own environments. + return self._next.copy() + + def step_async(self, actions: np.ndarray) -> None: + self._actions = np.asarray(actions, dtype=np.float32).reshape( + self.num_envs, 1, -1 + ) + + def step_wait(self): + results = self.server.step(self._actions) + obs = stack_states([r.next_obs for r in results], self.state_keys) + rewards = np.asarray([r.reward for r in results], dtype=np.float32) + terminated = np.asarray([r.terminated for r in results], dtype=np.bool_) + truncated = np.asarray([r.truncated for r in results], dtype=np.bool_) + dones = terminated | truncated + self._stats.add(rewards) + infos: list[dict[str, Any]] = [{} for _ in range(self.num_envs)] + for i in np.flatnonzero(dones): + infos[i]["terminal_observation"] = stack_states( + [results[i].feedback_obs], self.state_keys + )[0] + infos[i]["TimeLimit.truncated"] = bool(truncated[i] and not terminated[i]) + infos[i]["episode"] = { + "r": float(self._stats.returns[i]), + "l": int(self._stats.lengths[i]), + "t": round(time.time() - self._started, 6), + } + self._stats.end(dones) + self._next = obs + return obs, rewards, dones, infos + + def close(self) -> None: + self.server.close() + + # Per-env attributes and methods have no meaning here: the envs are + # other processes, reached only through the protocol. + def get_attr(self, attr_name: str, indices=None) -> list[Any]: + return [getattr(self, attr_name, None)] * len(self._get_indices(indices)) + + def set_attr(self, attr_name: str, value: Any, indices=None) -> None: + return None + + def env_method(self, method_name: str, *args, indices=None, **kwargs) -> list[Any]: + raise NotImplementedError( + f"{method_name}: a PlugRL env client's environment is not reachable " + "from the trainer except through the protocol" + ) + + def env_is_wrapped(self, wrapper_class, indices=None) -> list[bool]: + return [False] * len(self._get_indices(indices)) diff --git a/src/plugrl_rlinf/server.py b/src/plugrl_bridges/server.py similarity index 88% rename from src/plugrl_rlinf/server.py rename to src/plugrl_bridges/server.py index f3bcce3..c1c157b 100644 --- a/src/plugrl_rlinf/server.py +++ b/src/plugrl_bridges/server.py @@ -22,6 +22,10 @@ It never invents data. A connection that closes mid-run fails the next `step` or `gather` with ClientLost, and the run stops there. + +With `episode_log` (or PLUGRL_BRIDGES_EPISODE_LOG set), it writes one CSV +line per episode that ends, counted from the rewards the clients sent: the +same record whichever trainer steps it. """ from __future__ import annotations @@ -29,6 +33,7 @@ import asyncio import dataclasses import logging +import os import threading from typing import Any @@ -61,7 +66,7 @@ class Full(ProtocolError): # websockets logs every connection at INFO; one line each is noise in RLinf's # log, and a client stuck reconnecting turns it into hundreds of MB. -_ws_logger = logging.getLogger("plugrl_rlinf.websockets") +_ws_logger = logging.getLogger("plugrl_bridges.websockets") _ws_logger.setLevel(logging.WARNING) @@ -81,6 +86,51 @@ class SlotResult: next_obs: dict # the observation the next step starts from: reset if done +EPISODE_LOG_ENV = "PLUGRL_BRIDGES_EPISODE_LOG" + + +class EpisodeLog: + """`step,slot,episode,return,length`, one line per episode that ends. + + `step` is the server's step on which it ended, counting from 1. A step is + one action chunk, so with `action_horizon` 1 steps and lengths are env + steps. `episode` counts each slot's episodes from 1. The return is the + sum of the rewards the client fed back, before any trainer touches them. + """ + + HEADER = "step,slot,episode,return,length\n" + + def __init__(self, path: str | os.PathLike, num_envs: int) -> None: + self._file = open(path, "w", encoding="utf-8", newline="") + self._file.write(self.HEADER) + self._file.flush() + self._returns = np.zeros(num_envs, dtype=np.float64) + self._lengths = np.zeros(num_envs, dtype=np.int64) + self._episodes = np.zeros(num_envs, dtype=np.int64) + self._step = 0 + + def record(self, results: list[SlotResult]) -> None: + self._step += 1 + ended = False + for slot, r in enumerate(results): + self._returns[slot] += r.reward + self._lengths[slot] += 1 + if r.terminated or r.truncated: + self._episodes[slot] += 1 + self._file.write( + f"{self._step},{slot},{self._episodes[slot]}," + f"{self._returns[slot]:.6f},{self._lengths[slot]}\n" + ) + self._returns[slot] = 0.0 + self._lengths[slot] = 0 + ended = True + if ended: + self._file.flush() + + def close(self) -> None: + self._file.close() + + def _int_vector(value, what: str) -> np.ndarray: if not ( isinstance(value, np.ndarray) and value.ndim == 1 and value.dtype.kind in "iu" @@ -189,15 +239,20 @@ def __init__( action_dim: int, timeout: float = 600.0, metadata: dict | None = None, + episode_log: str | os.PathLike | None = None, ) -> None: self.host, self.port = host, port + episode_log = episode_log or os.environ.get(EPISODE_LOG_ENV) or None + self._episode_log = ( + EpisodeLog(episode_log, num_envs) if episode_log is not None else None + ) self.num_envs = num_envs self.action_horizon = action_horizon self.action_dim = action_dim self.timeout = timeout self.metadata = { "protocol_version": 1, - "server": "plugrl-rlinf", + "server": "plugrl-bridges", "action_horizon": action_horizon, "action_dim": action_dim, "num_envs": num_envs, @@ -237,10 +292,16 @@ def step(self, actions: np.ndarray) -> list[SlotResult]: expected = (self.num_envs, self.action_horizon, self.action_dim) if actions.shape != expected: raise ValueError(f"actions must be {expected}, got {actions.shape}") - return self._call(self._step(actions)) + results = self._call(self._step(actions)) + if self._episode_log is not None: + self._episode_log.record(results) + return results def close(self) -> None: """End the run: every client gets plugrl-server-stop.""" + if self._episode_log is not None: + self._episode_log.close() + self._episode_log = None if self._loop is None: return try: @@ -467,7 +528,7 @@ def _check_infer(self, c: _Connection, message: dict) -> list[dict]: # environments were (RLinf collects the worker's stdout). peer = getattr(c.ws, "remote_address", None) print( - f"plugrl-rlinf: slots {c.slots[0]}-{c.slots[-1]} <- {peer}", + f"plugrl-bridges: slots {c.slots[0]}-{c.slots[-1]} <- {peer}", flush=True, ) return rows diff --git a/src/plugrl_rlinf/__init__.py b/src/plugrl_rlinf/__init__.py deleted file mode 100644 index 0e5f6d1..0000000 --- a/src/plugrl_rlinf/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -"""RLinf trains environments that PlugRL env clients run, through the PlugRL protocol.""" - -from plugrl_rlinf.server import BridgeServer, ClientLost, SlotResult - -__all__ = ["BridgeServer", "ClientLost", "SlotResult", "PlugRLEnv"] -__version__ = "0.1.0" - - -def __getattr__(name: str): - # PlugRLEnv needs torch; the protocol server does not. - if name == "PlugRLEnv": - from plugrl_rlinf.env import PlugRLEnv - - return PlugRLEnv - raise AttributeError(name) diff --git a/tests/test_adapters.py b/tests/test_adapters.py new file mode 100644 index 0000000..9238804 --- /dev/null +++ b/tests/test_adapters.py @@ -0,0 +1,160 @@ +"""The gymnasium and Stable-Baselines3 adapters, against fake clients that speak the real protocol.""" + +from __future__ import annotations + +import socket + +import gymnasium as gym +import numpy as np +import pytest +from test_bridge import FakeClient + +from plugrl_protocol.websocket_protocol import SERVER_STOP_REASON + + +def _free_port() -> int: + with socket.socket() as s: + s.bind(("127.0.0.1", 0)) + return s.getsockname()[1] + + +ACTIONS = gym.spaces.Box(-1.0, 1.0, (2,), np.float32) + + +@pytest.fixture +def port(): + return _free_port() + + +def _connect_then(port, n, make, **client_kwargs): + """Start a fake client, then the adapter, which blocks until it connects.""" + client = FakeClient(port, n, **client_kwargs) + # The adapter's server must be listening first; FakeClient retries are + # not built in, so start it just after the server comes up. + import threading + + holder = {} + + def build(): + holder["env"] = make() + + t = threading.Thread(target=build) + t.start() + import time + + for _ in range(200): + try: + with socket.create_connection(("127.0.0.1", port), timeout=0.1): + break + except OSError: + time.sleep(0.02) + client.start() + t.join(20) + return holder["env"], client + + +def test_gym_vector_env_reads_its_spaces_off_the_clients_and_steps(port): + from plugrl_bridges.gym import PlugRLVectorEnv + + env, client = _connect_then( + port, 2, + lambda: PlugRLVectorEnv(num_envs=2, action_space=ACTIONS, port=port, host="127.0.0.1", timeout=20), + episode=[2, 3], + ) # fmt: skip + try: + assert env.single_observation_space.shape == (2,) + assert env.observation_space.shape == (2, 2) + assert env.action_space.shape == (2, 2) + obs, _ = env.reset() + assert obs[:, 0].tolist() == [0, 10] + obs, rewards, term, trunc, infos = env.step(np.ones((2, 2))) + assert rewards.tolist() == [2.0, 2.0] and "final_obs" not in infos + obs, rewards, term, trunc, infos = env.step(np.ones((2, 2))) + # Env 0 ended: SAME_STEP autoreset, the terminal observation in infos. + assert term.tolist() == [True, False] + assert infos["_final_obs"].tolist() == [True, False] + assert infos["final_obs"][0].tolist() == [-1.0, -2.0] + assert infos["final_obs"][1] is None + assert obs[:, 0].tolist() == [0, 12] + # Its own episode statistics, from the raw rewards: 2 + 2 over 2 steps. + assert infos["_episode"].tolist() == [True, False] + assert infos["episode"]["r"][0] == 4.0 and infos["episode"]["l"][0] == 2 + # The next episode counts from its first step (gymnasium's own vector + # RecordEpisodeStatistics would drop it under SAME_STEP). + for _ in range(2): + obs, rewards, term, trunc, infos = env.step(np.ones((2, 2))) + assert infos["_episode"].tolist() == [True, False] + assert infos["episode"]["r"][0] == 4.0 and infos["episode"]["l"][0] == 2 + finally: + env.close() + client.join(5) + assert client.close_reason == SERVER_STOP_REASON + + +def test_normalize_observation_normalizes_final_obs_with_the_same_statistics(port): + """gymnasium's vector observation wrappers refuse SAME_STEP; ours handles it.""" + from plugrl_bridges.gym import NormalizeObservation, PlugRLVectorEnv + + env, client = _connect_then( + port, 2, + lambda: PlugRLVectorEnv(num_envs=2, action_space=ACTIONS, port=port, host="127.0.0.1", timeout=20), + episode=[2, 3], + ) # fmt: skip + wrapped = NormalizeObservation(env) + try: + wrapped.reset() + wrapped.step(np.ones((2, 2))) + rms = wrapped.obs_rms + mean, var = rms.mean.copy(), rms.var.copy() + obs, _, term, _, infos = wrapped.step(np.ones((2, 2))) + assert term.tolist() == [True, False] + # The same update that normalized obs normalized the final one. + mean, var = wrapped.obs_rms.mean, wrapped.obs_rms.var + expected = (np.asarray([-1.0, -2.0]) - mean) / np.sqrt(var + 1e-8) + np.testing.assert_allclose(infos["final_obs"][0], expected, rtol=1e-5) + assert infos["final_obs"][1] is None + finally: + wrapped.close() + + +def test_sb3_vec_env_reports_episodes_as_sb3_expects(port): + from plugrl_bridges.sb3 import PlugRLVecEnv + + env, client = _connect_then( + port, 2, + lambda: PlugRLVecEnv(num_envs=2, action_space=ACTIONS, port=port, host="127.0.0.1", timeout=20), + episode=[2, 3], + ) # fmt: skip + try: + assert env.observation_space.shape == (2,) + assert env.reset()[:, 0].tolist() == [0, 10] + env.step(np.ones((2, 2))) + obs, rewards, dones, infos = env.step(np.ones((2, 2))) + assert dones.tolist() == [True, False] + assert infos[0]["terminal_observation"].tolist() == [-1.0, -2.0] + assert infos[0]["TimeLimit.truncated"] is False # it terminated + assert infos[0]["episode"]["r"] == 4.0 and infos[0]["episode"]["l"] == 2 + assert "episode" not in infos[1] + assert obs[:, 0].tolist() == [0, 12] + finally: + env.close() + + +def test_sb3_ppo_learns_on_it_without_complaint(port): + """Not whether it learns (that is an experiment), but that SB3 runs on it.""" + from stable_baselines3 import PPO + + from plugrl_bridges.sb3 import PlugRLVecEnv + + env, client = _connect_then( + port, 2, + lambda: PlugRLVecEnv(num_envs=2, action_space=ACTIONS, port=port, host="127.0.0.1", timeout=20), + episode=[5, 7], + ) # fmt: skip + try: + model = PPO("MlpPolicy", env, n_steps=16, batch_size=16, n_epochs=1, verbose=0) + model.learn(total_timesteps=64) + assert model.num_timesteps >= 64 + assert len(model.ep_info_buffer) > 0 # SB3 saw the episodes end + finally: + env.close() diff --git a/tests/test_bridge.py b/tests/test_bridge.py index abccf67..682506c 100644 --- a/tests/test_bridge.py +++ b/tests/test_bridge.py @@ -19,8 +19,8 @@ from plugrl_protocol import msgpack_numpy from plugrl_protocol.websocket_protocol import SERVER_RESYNC_REASON, SERVER_STOP_REASON -from plugrl_rlinf import ClientLost -from plugrl_rlinf.env import PlugRLEnv +from plugrl_bridges import ClientLost +from plugrl_bridges.rlinf import PlugRLEnv def _free_port() -> int: @@ -222,6 +222,50 @@ def test_an_ended_episode_reports_its_terminal_observation_and_statistics(port): env.close() +def test_the_episode_log_counts_each_episode_from_the_clients_rewards(port, tmp_path): + log = tmp_path / "episodes.csv" + env = _env(port, 2, episode_log=str(log)) + client = FakeClient(port, 2, episode=[2, 3]) + client.start() + try: + env.reset() + for k in range(1, 7): # each env's reward on step k is 2k + env.chunk_step(torch.full((2, 1, 2), float(k))) + finally: + env.close() + lines = log.read_text(encoding="utf-8").splitlines() + assert lines[0] == "step,slot,episode,return,length" + rows = [ + (int(s), int(slot), int(e), float(r), int(n)) + for s, slot, e, r, n in (line.split(",") for line in lines[1:]) + ] + assert rows == [ + (2, 0, 1, 2 + 4, 2), + (3, 1, 1, 2 + 4 + 6, 3), + (4, 0, 2, 6 + 8, 2), + (6, 0, 3, 10 + 12, 2), + (6, 1, 2, 8 + 10 + 12, 3), + ] + + +def test_the_episode_log_path_can_come_from_the_environment( + port, tmp_path, monkeypatch +): + from plugrl_bridges.server import EPISODE_LOG_ENV + + log = tmp_path / "from-env.csv" + monkeypatch.setenv(EPISODE_LOG_ENV, str(log)) + env = _env(port, 1) + client = FakeClient(port, 1, episode=[1]) + client.start() + try: + env.reset() + env.chunk_step(torch.ones((1, 1, 2))) + finally: + env.close() + assert log.read_text(encoding="utf-8").splitlines()[1:] == ["1,0,1,2.000000,1"] + + def test_a_client_that_reuses_observations_gets_the_same_observations(port): env = _env(port, 2) client = FakeClient(port, 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