From a85a7b8d896cf28df376f7ec7e17724bc85b4c26 Mon Sep 17 00:00:00 2001 From: RobinMeersman <77965843+RobinMeersman@users.noreply.github.com> Date: Thu, 26 Mar 2026 09:53:54 +0100 Subject: [PATCH] Environment setup + train loop (#5) Mujoco environment setup (vectorized on GPU) + training loop + simulate script --- .gitignore | 5 +- README.md | 17 + docs/api/environment.md | 24 + docs/api/train_simulate.md | 30 + docs/design_decisions/.keep | 0 experiments/simulate.py | 100 ++ pyproject.toml | 7 +- ruff.toml | 2 + src/__init__.py | 0 src/brittle_star_project/__init__.py | 14 + .../dataclasses/EpisodeStatistics.py | 10 + .../dataclasses/PPOArgs.py | 103 ++ .../dataclasses/__init__.py | 8 + .../environment/BrittleStarJaxEnvWrapper.py | 78 ++ .../environment/__init__.py | 15 + .../environment/env_config.py | 53 + .../environment/env_types.py | 21 + .../environment/env_wrapper.py | 87 ++ .../environment/factory.py | 106 ++ src/brittle_star_project/render/__init__.py | 0 src/brittle_star_project/render/renderer.py | 75 ++ src/brittle_star_project/rl/DummyAgent.py | 71 ++ src/brittle_star_project/rl/__init__.py | 25 + src/brittle_star_project/rl/base.py | 162 +++ .../rl/random_policy_model.py | 52 + src/train.py | 377 +++++++ uv.lock | 947 +++++++++++++----- 27 files changed, 2155 insertions(+), 234 deletions(-) create mode 100644 docs/api/environment.md create mode 100644 docs/api/train_simulate.md create mode 100644 docs/design_decisions/.keep create mode 100644 experiments/simulate.py create mode 100644 src/__init__.py create mode 100644 src/brittle_star_project/__init__.py create mode 100644 src/brittle_star_project/dataclasses/EpisodeStatistics.py create mode 100644 src/brittle_star_project/dataclasses/PPOArgs.py create mode 100644 src/brittle_star_project/dataclasses/__init__.py create mode 100644 src/brittle_star_project/environment/BrittleStarJaxEnvWrapper.py create mode 100644 src/brittle_star_project/environment/__init__.py create mode 100644 src/brittle_star_project/environment/env_config.py create mode 100644 src/brittle_star_project/environment/env_types.py create mode 100644 src/brittle_star_project/environment/env_wrapper.py create mode 100644 src/brittle_star_project/environment/factory.py create mode 100644 src/brittle_star_project/render/__init__.py create mode 100644 src/brittle_star_project/render/renderer.py create mode 100644 src/brittle_star_project/rl/DummyAgent.py create mode 100644 src/brittle_star_project/rl/__init__.py create mode 100644 src/brittle_star_project/rl/base.py create mode 100644 src/brittle_star_project/rl/random_policy_model.py create mode 100644 src/train.py diff --git a/.gitignore b/.gitignore index da01ac7..8204ce9 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,7 @@ +# Model files +artifacts/* +runs/* + # Python-generated files __pycache__/ *.py[oc] @@ -515,4 +519,3 @@ Icon Network Trash Folder Temporary Items .apdisk - diff --git a/README.md b/README.md index e69de29..47b0552 100644 --- a/README.md +++ b/README.md @@ -0,0 +1,17 @@ +# Brittle Star + +## Usage + +### UV + +To set up the UV module, you can run the following command: + +```bash +uv sync --frozen +``` + +example command: + +```bash +uv run src/train.py --model_name my_model --epochs 50 --batch_size 32 +``` diff --git a/docs/api/environment.md b/docs/api/environment.md new file mode 100644 index 0000000..470732d --- /dev/null +++ b/docs/api/environment.md @@ -0,0 +1,24 @@ +# Brittle star environment + +## Creation +The environment package contains a factory class `BrittleStarEnvFactory` +that creates instances of the environment/morphologies/... It uses the +configuration classes defined in `env_config.py` to create the instances. + +## Configuration +The data classes in `env_config` have default values as stated in the tutorials. +* MorphologyConfig: configuration for the morphology of the brittle star. Contains +number of arms, number of segments per arm, and control mode. +* ArenaConfig: configuration for the arena. Sets the size of the arena, whether to +set the ground floor to sand, attach a target and sizes of the walls. +* EnvConfig: configuration for the environment. These set shared settings +such as camera locations, simulation time and the task. + +## Backend and Task enums +The Backend enum specifies either an MJC or MJX backend. +* MJC: runs on CPU +* MJX: uses jax on the gpu + +The Task enum specifies which task to use. 2 items are present: +* DIRECTED_LOCOMOTION: move to a target location +* LIGHT_ESCAPE: situation where the robot must move to a darker location diff --git a/docs/api/train_simulate.md b/docs/api/train_simulate.md new file mode 100644 index 0000000..9b299a9 --- /dev/null +++ b/docs/api/train_simulate.md @@ -0,0 +1,30 @@ +# Training and Simulation for Brittle Star Models + +## Training a model + +To train a model, you can use the `train.py` script. This script allows to pass some parameters to customize the training process: + +- `--out`: The output path where the trained model will be saved. +- `--model_type`: The type of model to train (e.g., `random`, ...) +- `--task`: The task to train on (e.g., `directed_locomotion`, ...) +- `--seed`: The random seed for reproducibility. +- `--epochs`: The number of epochs to train for. + +This will then train the specified model on the specified task for the given number of epochs and save the trained model to the specified output path. + +```bash +python train.py --out artifacts/my_model --model-type random --task directed_locomotion --seed 0 --epochs 50 +``` + +## Simulating a model + +In order to simulate and view the behavior of a trained model, you can use the `simulate.py` script. This script allows you to specify the path to a trained model and will launch a simulation using that model. This script has the following parameters: + +- `--model`: The path to the trained model artifact to simulate. +- `--model-type`: The type of model to simulate (e.g., `random`, ...) +- `--task`: The task to simulate (e.g., `directed_locomotion`, ...) +- `--seed`: The random seed for reproducibility. + +```bash +python simulate.py --model artifacts/my_model --model-type random --task directed_locomotion --seed 0 +``` \ No newline at end of file diff --git a/docs/design_decisions/.keep b/docs/design_decisions/.keep new file mode 100644 index 0000000..e69de29 diff --git a/experiments/simulate.py b/experiments/simulate.py new file mode 100644 index 0000000..346bcda --- /dev/null +++ b/experiments/simulate.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +import argparse +from pathlib import Path + +from brittle_star_project import ( + ArenaConfig, + Backend, + BrittleStarEnv, + BrittleStarEnvFactory, + EnvConfig, + MorphologyConfig, + Task, +) +from brittle_star_project.rl import RLModel # imports concrete models via rl.__init__ +from brittle_star_project.rl.base import get_rl_model_registry +from brittle_star_project.renderer import SimulationConfig, simulate_policy + +MODEL_BY_NAME = get_rl_model_registry() +MODEL_OPTIONS = sorted(MODEL_BY_NAME) + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser(description="Simulate a trained policy in the MuJoCo viewer.") + p.add_argument( + "--model", + type=str, + default=None, + help="Path to a saved model artifact. If omitted, a model is created from --model-type.", + ) + p.add_argument( + "--model-type", + choices=MODEL_OPTIONS, + default="random", + help="Which model class to instantiate when --model is omitted.", + ) + p.add_argument( + "--task", + choices=[t.value for t in Task], + default=Task.DIRECTED_LOCOMOTION.value, + ) + p.add_argument("--seed", type=int, default=None) + return p.parse_args() + + +def main() -> None: + args = parse_args() + + # ======= ENVIRONMENT SETUP ======= + + backend = Backend.MJC + task = Task(args.task) + + morphology_cfg = MorphologyConfig() + arena_cfg = ArenaConfig(attach_target=(task == Task.DIRECTED_LOCOMOTION)) + env_cfg = EnvConfig(task=task) + + factory = BrittleStarEnvFactory() + raw_env = factory.create_environment(backend, morphology_cfg, arena_cfg, env_cfg) + env = BrittleStarEnv(raw_env, backend=backend, config=env_cfg) + + seed_for_env = int(args.seed) if args.seed is not None else 0 + state = env.reset(seed=seed_for_env) + + # ======= MODEL SETUP ======= + + # Extract the number of actuators (nu) from the environment's model, so we can pass it to the + # policy/model. + nu = int(state.mj_model.nu) + + if args.model is not None: + model_path = Path(args.model) + policy = RLModel.load(model_path) + if hasattr(policy, "nu"): + policy.nu = nu + else: + model_cls = MODEL_BY_NAME[str(args.model_type)] + policy = model_cls(seed=seed_for_env) + if hasattr(policy, "nu"): + policy.nu = nu + + # If the policy/model has a `seed` attribute, use the provided seed (or default) to reset it. + default_seed = int(getattr(policy, "seed", seed_for_env)) + if args.seed is not None and hasattr(policy, "reset"): + policy.reset(int(args.seed)) + + # ======= SIMULATION ======= + + rollout_cfg = SimulationConfig( + realtime=True, + seed=int(args.seed) if args.seed is not None else default_seed, + ) + + simulate_policy(policy, rollout_cfg, state) + + env.close() + + +if __name__ == "__main__": + main() diff --git a/pyproject.toml b/pyproject.toml index 240725d..43c977b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,16 +3,21 @@ name = "2026sel3-project" version = "0.1.0" description = "Add your description here" readme = "README.md" -requires-python = ">=3.12, <3.13" +requires-python = ">= 3.12, < 3.13" dependencies = [ "biorobot==0.4.2", + "cleanrl>=0.4.8", "evosax==0.2.0", + "flax>=0.12.2", + "gymnasium>=1.2.3", "ipykernel==7.2.0", "jax==0.9.0.1", "matplotlib==3.10.8", "mediapy==1.2.6", + "optax>=0.2.6", "pyopengl>=3.1.10", "pyopengl-accelerate>=3.1.10", + "tyro>=1.0.10", "wandb==0.24.2", ] diff --git a/ruff.toml b/ruff.toml index 7bc4795..b19b931 100644 --- a/ruff.toml +++ b/ruff.toml @@ -1,3 +1,5 @@ +line-length = 100 + [lint] extend-select = [ # "PLC0103", # invalid-name diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/brittle_star_project/__init__.py b/src/brittle_star_project/__init__.py new file mode 100644 index 0000000..b6b123a --- /dev/null +++ b/src/brittle_star_project/__init__.py @@ -0,0 +1,14 @@ +from .environment.env_types import Backend, Task +from .environment.env_config import ArenaConfig, EnvConfig, MorphologyConfig +from .environment.factory import BrittleStarEnvFactory +from .environment.env_wrapper import BrittleStarEnv + +__all__ = [ + "ArenaConfig", + "Backend", + "BrittleStarEnv", + "BrittleStarEnvFactory", + "EnvConfig", + "MorphologyConfig", + "Task", +] diff --git a/src/brittle_star_project/dataclasses/EpisodeStatistics.py b/src/brittle_star_project/dataclasses/EpisodeStatistics.py new file mode 100644 index 0000000..0b2832f --- /dev/null +++ b/src/brittle_star_project/dataclasses/EpisodeStatistics.py @@ -0,0 +1,10 @@ +import flax.struct +import jax.numpy as jnp + + +@flax.struct.dataclass +class EpisodeStatistics: + episode_returns: jnp.array + episode_lengths: jnp.array + returned_episode_returns: jnp.array + returned_episode_lengths: jnp.array diff --git a/src/brittle_star_project/dataclasses/PPOArgs.py b/src/brittle_star_project/dataclasses/PPOArgs.py new file mode 100644 index 0000000..4fc3f2e --- /dev/null +++ b/src/brittle_star_project/dataclasses/PPOArgs.py @@ -0,0 +1,103 @@ +from dataclasses import dataclass + + +@dataclass +class PPOArgs: + """ + source: https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_xla_jax_scan.py + """ + + # the name of this experiment + exp_name: str = "brittle_star_ppo" + + # seed of the experiment + seed: int = 1 + + # if toggled, `torch.backends.cudnn.deterministic=False` + torch_deterministic: bool = True + + # if toggled, cuda will be enabled by default + cuda: bool = True + + # if toggled, this experiment will be tracked with Weights and Biases + track: bool = False + + # the wandb's project name + wandb_project_name: str = "PPO-Modularity" + + # the entity (team) of wandb's project + wandb_entity: str | None = None + + # whether to capture videos of the agent performances (check out `videos` folder) + capture_video: bool = False + + # whether to save model into the `runs/{run_name}` folder + save_model: bool = True + + # whether to upload the saved model to huggingface + upload_model: bool = False + + # the user or org name of the model repository from the Hugging Face Hub + hf_entity: str = "" + + # ==== Algorithm specific dataclasses ==== + # the id of the environment + env_id: str = "" # todo + + # total timesteps of the experiments + total_timesteps: int = 10000000 + + # the learning rate of the optimizer + learning_rate: float = 2.5e-4 + + # the number of parallel game environments + num_envs: int = 16 + + # the number of steps to run in each environment per policy rollout + num_steps: int = 128 + + # Toggle learning rate annealing for policy and value networks + anneal_lr: bool = True + + # the discount factor gamma + gamma: float = 0.99 + + # the lambda for the general advantage estimation + gae_lambda: float = 0.95 + + # the number of mini-batches + num_minibatches: int = 4 + + # the K epochs to update the policy + update_epochs: int = 4 + + # Toggles advantages normalization + norm_adv: bool = True + + # the surrogate clipping coefficient + clip_coef: float = 0.1 + + # Toggles whether or not to use a clipped loss for the value function, as per the paper. + clip_vloss: bool = True + + # coefficient of the entropy + ent_coef: float = 0.01 + + # coefficient of the value function + vf_coef: float = 0.5 + + # the maximum norm for the gradient clipping + max_grad_norm: float = 0.5 + + # the target KL divergence threshold + target_kl: float | None = None + + # ==== to be filled in runtime ==== + # the batch size (computed in runtime) + batch_size: int = 0 + + # the mini-batch size (computed in runtime) + minibatch_size: int = 0 + + # the number of iterations (computed in runtime) + num_iterations: int = 0 diff --git a/src/brittle_star_project/dataclasses/__init__.py b/src/brittle_star_project/dataclasses/__init__.py new file mode 100644 index 0000000..3501b2b --- /dev/null +++ b/src/brittle_star_project/dataclasses/__init__.py @@ -0,0 +1,8 @@ +from .PPOArgs import PPOArgs +from .EpisodeStatistics import EpisodeStatistics + + +__all__ = [ + "PPOArgs", + "EpisodeStatistics", +] diff --git a/src/brittle_star_project/environment/BrittleStarJaxEnvWrapper.py b/src/brittle_star_project/environment/BrittleStarJaxEnvWrapper.py new file mode 100644 index 0000000..1ea07c7 --- /dev/null +++ b/src/brittle_star_project/environment/BrittleStarJaxEnvWrapper.py @@ -0,0 +1,78 @@ +import jax +import jax.numpy as jnp + +from brittle_star_project import ( + EnvConfig, + BrittleStarEnvFactory, + MorphologyConfig, + ArenaConfig, + Backend, +) + + +class BrittleStarJaxEnvWrapper: + def __init__( + self, + morphology: MorphologyConfig, + arena: ArenaConfig, + env_config: EnvConfig, + num_envs: int, + backend: Backend = Backend.MJX, + ): + self._morphology = morphology + self._arena = arena + self._env_config = env_config + self._backend = backend + self._num_envs = num_envs + self._env = BrittleStarEnvFactory.create_environment( + self._backend, self._morphology, self._arena, self._env_config + ) + + self._vectorized_reset = jax.jit(jax.vmap(self._env.reset)) + self._vectorized_step = jax.jit(jax.vmap(self._env.step)) + self._vectorized_action_sample = jax.jit(jax.vmap(self._env.action_space.sample)) + + self._action_rng = None + + @property + def backend(self): + return self._backend + + @property + def raw(self): + return self._env + + @property + def single_action_space(self): + return self._env.action_space + + @property + def single_observation_space(self): + return self._env.observation_space + + def reset(self, seed: int = 0): + self._action_rng, env_rng = jax.random.split(jax.random.PRNGKey(seed), 2) + env_rngs = jnp.array(jax.random.split(env_rng, self._num_envs)) + return self._vectorized_reset(rng=env_rngs) + + def sample_actions(self): + assert self._action_rng is not None, "Call reset() before sample_actions()" + self._action_rng, *sub_rngs = jnp.array( + jax.random.split(self._action_rng, self._num_envs + 1) + ) + return self._vectorized_action_sample(rng=jnp.array(sub_rngs)) + + def step(self, state, action): + return self._vectorized_step(state=state, action=action) + + def close(self): + self._env.close() + + @staticmethod + def default(num_envs: int, backend: Backend = Backend.MJX) -> "BrittleStarJaxEnvWrapper": + morphology = MorphologyConfig() + arena = ArenaConfig() + env_config = EnvConfig() + return BrittleStarJaxEnvWrapper( + morphology, arena, env_config, num_envs=num_envs, backend=backend + ) diff --git a/src/brittle_star_project/environment/__init__.py b/src/brittle_star_project/environment/__init__.py new file mode 100644 index 0000000..78896cb --- /dev/null +++ b/src/brittle_star_project/environment/__init__.py @@ -0,0 +1,15 @@ +from .env_config import ArenaConfig, EnvConfig, MorphologyConfig +from .env_types import Backend, Task +from .env_wrapper import BrittleStarEnv, StepResult +from .factory import BrittleStarEnvFactory + +__all__ = [ + "ArenaConfig", + "EnvConfig", + "MorphologyConfig", + "Backend", + "Task", + "BrittleStarEnv", + "StepResult", + "BrittleStarEnvFactory", +] diff --git a/src/brittle_star_project/environment/env_config.py b/src/brittle_star_project/environment/env_config.py new file mode 100644 index 0000000..f50f4d8 --- /dev/null +++ b/src/brittle_star_project/environment/env_config.py @@ -0,0 +1,53 @@ +from __future__ import annotations + +from dataclasses import dataclass, field + +from .env_types import Task + + +@dataclass(frozen=True, slots=True) +class MorphologyConfig: + num_arms: int = 5 + num_segments_per_arm: int = 4 + use_p_control: bool = True + use_torque_control: bool = False + + +@dataclass(frozen=True, slots=True) +class ArenaConfig: + size: tuple[float, float] = (10.0, 5.0) + sand_ground_color: bool = True + attach_target: bool = True + wall_height: float = 1.5 + wall_thickness: float = 0.1 + + +@dataclass(frozen=True, slots=True) +class EnvConfig: + """Shared environment settings. + + Note: Some tasks have additional parameters (see fields below). + """ + + task: Task = Task.DIRECTED_LOCOMOTION + + simulation_time: float = 5.0 + num_physics_steps_per_control_step: int = 10 + time_scale: int = 2 + + camera_ids: list[int] = field(default_factory=lambda: [0, 1]) + # (height, width) + render_size: tuple[int, int] = (480, 640) + + joint_randomization_noise_scale: float = 0.0 + + # Directed locomotion + target_distance: float = 3.0 + + # Light escape + # Per docs in upstream env config: integer factors of 200. + light_perlin_noise_scale: int = 0 + + @staticmethod + def from_json(path: str) -> EnvConfig: + pass diff --git a/src/brittle_star_project/environment/env_types.py b/src/brittle_star_project/environment/env_types.py new file mode 100644 index 0000000..d1be3d5 --- /dev/null +++ b/src/brittle_star_project/environment/env_types.py @@ -0,0 +1,21 @@ +from __future__ import annotations + +from enum import Enum + + +class Backend(str, Enum): + """Physics backend. + + - MJC: MuJoCo C engine + - MJX: MuJoCo XLA (JAX) engine + """ + + MJC = "MJC" + MJX = "MJX" + + +class Task(str, Enum): + """Which brittle-star task/environment to instantiate.""" + + DIRECTED_LOCOMOTION = "directed_locomotion" + LIGHT_ESCAPE = "light_escape" diff --git a/src/brittle_star_project/environment/env_wrapper.py b/src/brittle_star_project/environment/env_wrapper.py new file mode 100644 index 0000000..f74a1ca --- /dev/null +++ b/src/brittle_star_project/environment/env_wrapper.py @@ -0,0 +1,87 @@ +from __future__ import annotations + +import inspect +from dataclasses import dataclass +from typing import Any + +import numpy as np + +from .env_config import EnvConfig +from .env_types import Backend + + +@dataclass(slots=True) +class StepResult: + state: Any + reward: float | None = None + terminated: bool | None = None + truncated: bool | None = None + info: dict[str, Any] | None = None + + +class BrittleStarEnv: + """Thin wrapper around the underlying DualMuJoCoEnvironment. + + Goal: hide backend-specific RNG setup and provide a stable place to plug in RL. + """ + + def __init__(self, env: Any, *, backend: Backend, config: EnvConfig) -> None: + self._env = env + self._backend = backend + self._config = config + + @property + def raw(self) -> Any: + return self._env + + @property + def backend(self) -> Backend: + return self._backend + + @property + def config(self) -> EnvConfig: + return self._config + + def make_rng(self, seed: int): + if self._backend == Backend.MJC: + return np.random.RandomState(seed) + + import jax + + return jax.random.PRNGKey(seed) + + def reset(self, *, seed: int = 0): + rng = self.make_rng(seed) + state = self._env.reset(rng=rng) + return state + + def render(self, *, state: Any): + return self._env.render(state=state) + + def close(self) -> None: + self._env.close() + + def step(self, *, state: Any, action: Any, rng: Any | None = None) -> StepResult: + """Best-effort step wrapper. + + Different env libraries return different tuples; we normalize common cases. + """ + + if not hasattr(self._env, "step"): + raise AttributeError("Underlying env has no step() method") + + step_fn = self._env.step + sig = inspect.signature(step_fn) + params = list(sig.parameters) + + # Common patterns: + # - step(state, action) + # - step(state, action, rng) + # - step(state, action, key) + # We pass rng only if the callable accepts a 3rd arg. + if len(params) >= 3 and rng is not None: + out = step_fn(state, action, rng) + else: + out = step_fn(state, action) + + return out diff --git a/src/brittle_star_project/environment/factory.py b/src/brittle_star_project/environment/factory.py new file mode 100644 index 0000000..1cf94f2 --- /dev/null +++ b/src/brittle_star_project/environment/factory.py @@ -0,0 +1,106 @@ +from __future__ import annotations + +from dataclasses import asdict + +from moojoco.environment.dual import DualMuJoCoEnvironment + +from .env_config import ArenaConfig, EnvConfig, MorphologyConfig +from .env_types import Backend, Task + + +class BrittleStarEnvFactory: + """Creates brittle-star morphology, arena, and task environment instances.""" + + @staticmethod + def create_morphology(config: MorphologyConfig): + from biorobot.brittle_star.mjcf.morphology.morphology import ( + MJCFBrittleStarMorphology, + ) + from biorobot.brittle_star.mjcf.morphology.specification.default import ( + default_brittle_star_morphology_specification, + ) + + spec = default_brittle_star_morphology_specification( + num_arms=config.num_arms, + num_segments_per_arm=config.num_segments_per_arm, + use_p_control=config.use_p_control, + use_torque_control=config.use_torque_control, + ) + return MJCFBrittleStarMorphology(specification=spec) + + @staticmethod + def create_arena(config: ArenaConfig): + from biorobot.brittle_star.mjcf.arena.aquarium import ( + AquariumArenaConfiguration, + MJCFAquariumArena, + ) + + arena_config = AquariumArenaConfiguration(**asdict(config)) + return MJCFAquariumArena(configuration=arena_config) + + @staticmethod + def create_environment_configuration(config: EnvConfig): + # Import locally so the project can still be imported without these deps. + from biorobot.brittle_star.environment.directed_locomotion.shared import ( + BrittleStarDirectedLocomotionEnvironmentConfiguration, + ) + from biorobot.brittle_star.environment.light_escape.shared import ( + BrittleStarLightEscapeEnvironmentConfiguration, + ) + + common = dict( + joint_randomization_noise_scale=config.joint_randomization_noise_scale, + render_mode="human", + simulation_time=config.simulation_time, + num_physics_steps_per_control_step=config.num_physics_steps_per_control_step, + time_scale=config.time_scale, + camera_ids=config.camera_ids, + render_size=config.render_size, + ) + + match config.task: + case Task.DIRECTED_LOCOMOTION: + return BrittleStarDirectedLocomotionEnvironmentConfiguration( + target_distance=config.target_distance, + **common, + ) + case Task.LIGHT_ESCAPE: + return BrittleStarLightEscapeEnvironmentConfiguration( + light_perlin_noise_scale=config.light_perlin_noise_scale, + **common, + ) + case _: + raise ValueError(f"Unsupported task: {config.task}") + + @staticmethod + def create_environment( + backend: Backend, + morphology_config: MorphologyConfig, + arena_config: ArenaConfig, + env_config: EnvConfig, + ) -> DualMuJoCoEnvironment: + from biorobot.brittle_star.environment.directed_locomotion.dual import ( + BrittleStarDirectedLocomotionEnvironment, + ) + from biorobot.brittle_star.environment.light_escape.dual import ( + BrittleStarLightEscapeEnvironment, + ) + + morphology = BrittleStarEnvFactory.create_morphology(morphology_config) + arena = BrittleStarEnvFactory.create_arena(arena_config) + env_configuration = BrittleStarEnvFactory.create_environment_configuration(env_config) + + match env_config.task: + case Task.DIRECTED_LOCOMOTION: + env_class = BrittleStarDirectedLocomotionEnvironment + case Task.LIGHT_ESCAPE: + env_class = BrittleStarLightEscapeEnvironment + case _: + raise ValueError(f"Unsupported task: {env_config.task}") + + return env_class.from_morphology_and_arena( + morphology=morphology, + arena=arena, + configuration=env_configuration, + backend=backend.value, + ) diff --git a/src/brittle_star_project/render/__init__.py b/src/brittle_star_project/render/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/brittle_star_project/render/renderer.py b/src/brittle_star_project/render/renderer.py new file mode 100644 index 0000000..4d97ef1 --- /dev/null +++ b/src/brittle_star_project/render/renderer.py @@ -0,0 +1,75 @@ +from __future__ import annotations + +import time +from dataclasses import dataclass +from typing import Any, Protocol + +import numpy as np + + +@dataclass +class SimulationConfig: + realtime: bool = True + seed: int = 0 + + +class ControlPolicy(Protocol): + def act(self, *, obs: np.ndarray | None = None, t: float = 0.0) -> np.ndarray: ... + + +def _default_observations(data: Any) -> np.ndarray: + qpos = np.asarray(data.qpos, dtype=np.float32).ravel() + qvel = np.asarray(data.qvel, dtype=np.float32).ravel() + return np.concatenate([qpos, qvel], axis=0) + + +def simulate_policy( + policy: ControlPolicy, + config: SimulationConfig, + state: Any | None = None, +) -> None: + """Open MuJoCo's native viewer and step using actions from a policy. + + This path drives MuJoCo physics directly (mj_step) and uses the policy output + as `data.ctrl`. + """ + + import mujoco.viewer + + model = state.mj_model + data = state.mj_data + + start = time.time() + with mujoco.viewer.launch_passive(model, data) as viewer: + while viewer.is_running(): + step_start = time.time() + + t = time.time() - start + + # Input vector for the policy + # TODO: custom input + obs = _default_observations(data) + + # Policy action + ctrl = policy.act(obs=obs, t=t) + + # Check if the policy output vector give an input for each actuator (nu) + # TODO: what if model trained on full morphology but we want to test on a damaged one? + # (nu mismatch) + if model.nu > 0: + ctrl = np.asarray(ctrl, dtype=np.float32).ravel() + if ctrl.shape != (model.nu,): + raise ValueError( + f"Policy returned ctrl shape {ctrl.shape}, expected ({model.nu},)" + ) + data.ctrl[:] = ctrl + + # Step the simulation and update the viewer + mujoco.mj_step(model, data) + viewer.sync() + + # If we're running in realtime mode, sleep to maintain real-time pacing. + if config.realtime: + remaining = model.opt.timestep - (time.time() - step_start) + if remaining > 0: + time.sleep(remaining) diff --git a/src/brittle_star_project/rl/DummyAgent.py b/src/brittle_star_project/rl/DummyAgent.py new file mode 100644 index 0000000..fff4068 --- /dev/null +++ b/src/brittle_star_project/rl/DummyAgent.py @@ -0,0 +1,71 @@ +from dataclasses import dataclass, fields + +import flax +import flax.linen as nn +import jax.numpy as jnp +import jax.tree_util +import numpy as np +from flax.linen.initializers import constant, orthogonal + + +class Network(nn.Module): + """ + Dummy model only used for testing purposes + + inspired by: https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_xla_jax_scan.py + """ + + hidden_dim: int = 195 + + @nn.compact + def __call__(self, x): + x = nn.Dense(self.hidden_dim, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0))( + x + ) + x = nn.relu(x) + x = nn.Dense(self.hidden_dim, kernel_init=orthogonal(np.sqrt(2)), bias_init=constant(0.0))( + x + ) + x = nn.relu(x) + return x + + +class Critic(nn.Module): + @nn.compact + def __call__(self, x): + return nn.Dense(1, kernel_init=orthogonal(1), bias_init=constant(0.0))(x) + + +class Actor(nn.Module): + action_dim: int + + @nn.compact + def __call__(self, x): + mean = nn.Dense(self.action_dim, kernel_init=orthogonal(0.01), bias_init=constant(0.0))(x) + log_std = self.param("log_std", nn.initializers.zeros, (self.action_dim,)) + return mean, log_std + + +@jax.tree_util.register_dataclass +@dataclass +class AgentParams: + network_params: flax.core.FrozenDict + actor_params: flax.core.FrozenDict + critic_params: flax.core.FrozenDict + + +@jax.tree_util.register_dataclass +@dataclass +class Storage: + obs: jnp.array + actions: jnp.array + logprobs: jnp.array + dones: jnp.array + values: jnp.array + advantages: jnp.array + returns: jnp.array + rewards: jnp.array + + def replace(self, **kwargs) -> "Storage": + fs = fields(self) + return Storage(**{f.name: kwargs.get(f.name, getattr(self, f.name)) for f in fs}) diff --git a/src/brittle_star_project/rl/__init__.py b/src/brittle_star_project/rl/__init__.py new file mode 100644 index 0000000..e58e1c6 --- /dev/null +++ b/src/brittle_star_project/rl/__init__.py @@ -0,0 +1,25 @@ +from .DummyAgent import Network, Critic, Actor, AgentParams, Storage +from .base import ( + RLAlgorithm, + RLModel, + Transition, + create_model, + register_rl_model, + registered_model_types, +) +from .random_policy_model import RandomPolicyModel + +__all__ = [ + "RLAlgorithm", + "RLModel", + "RandomPolicyModel", + "Transition", + "create_model", + "register_rl_model", + "registered_model_types", + "Network", + "Critic", + "Actor", + "AgentParams", + "Storage", +] diff --git a/src/brittle_star_project/rl/base.py b/src/brittle_star_project/rl/base.py new file mode 100644 index 0000000..8e3c439 --- /dev/null +++ b/src/brittle_star_project/rl/base.py @@ -0,0 +1,162 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from dataclasses import dataclass +import json +from pathlib import Path +from typing import Any + + +@dataclass(frozen=True, slots=True) +class Transition: + """A minimal transition container for RL. + + This is intentionally generic because the underlying env state type may be a + JAX pytree, a numpy struct, or something library-specific. + """ + + obs: Any + action: Any + reward: float + next_obs: Any + terminated: bool + truncated: bool + info: dict[str, Any] | None = None + + +class RLAlgorithm(ABC): + """Insertable RL algorithm interface.""" + + @abstractmethod + def select_action(self, *, obs: Any, rng: Any | None = None) -> Any: + raise NotImplementedError + + def observe(self, transition: Transition) -> None: + """Optional hook to store transitions.""" + + def update(self, *, rng: Any | None = None) -> dict[str, float]: + """Optional hook to run one training update.""" + + return {} + + def save(self, path: str) -> None: + raise NotImplementedError("Save not implemented") + + def load(self, path: str) -> None: + raise NotImplementedError("Load not implemented") + + +_RL_MODEL_REGISTRY: dict[str, type["RLModel"]] = {} + + +def registered_model_types() -> list[str]: + return sorted(_RL_MODEL_REGISTRY) + + +def create_model(type_name: str, *, payload: dict[str, Any]) -> "RLModel": + model_cls = _RL_MODEL_REGISTRY.get(type_name) + if model_cls is None: + known = ", ".join(sorted(_RL_MODEL_REGISTRY)) or "" + raise ValueError(f"Unknown RLModel type '{type_name}'. Known: {known}") + return model_cls.from_payload(payload) + + +def get_rl_model_registry() -> dict[str, type["RLModel"]]: + """Return a copy of the current RLModel registry. + + The registry is populated by importing concrete model modules that use the + `@register_rl_model(...)` decorator. + """ + + return dict(_RL_MODEL_REGISTRY) + + +def register_rl_model(*type_names: str): + """Decorator to register an `RLModel` for generic loading. + + Concrete model modules should apply this decorator, so `base.py` never needs + to import concrete models (avoids circular imports). + """ + + if not type_names: + raise TypeError("register_rl_model() requires at least one type name") + + primary = type_names[0] + + def _decorator(cls: type[RLModel]): + for name in type_names: + _RL_MODEL_REGISTRY[name] = cls + cls.type_name = primary + return cls + + return _decorator + + +class RLModel(ABC): + """Serializable policy/model interface. + + This is the artifact that `train.py` writes and `simulate.py` loads. + """ + + # Overwritten by the `@register_rl_model(...)` decorator. + type_name: str = "RLModel" + + def reset(self, seed: int | None = None) -> None: + """Optional hook for RNG/stateful models.""" + + @abstractmethod + def act(self, *, obs: Any | None = None, t: float = 0.0) -> Any: + raise NotImplementedError + + def train(self, *, env: Any, num_epochs: int = 1) -> None: + """Optional training hook. + + Many models won't learn; for those this can be a no-op. + """ + + _ = (env, num_epochs) + + def to_payload(self) -> dict[str, Any]: + """Return JSON-serializable model parameters.""" + + return {} + + @classmethod + def from_payload(cls, payload: dict[str, Any]) -> "RLModel": + """Reconstruct a model from `to_payload()` output.""" + + return cls(**payload) # type: ignore[arg-type] + + def save(self, path: str | Path) -> Path: + out = Path(path) + out.parent.mkdir(parents=True, exist_ok=True) + doc = { + "type": self.type_name, + "version": 1, + "payload": self.to_payload(), + } + out.write_text(json.dumps(doc, indent=2, sort_keys=True) + "\n") + return out + + @classmethod + def load(cls, path: str | Path) -> "RLModel": + p = Path(path) + doc = json.loads(p.read_text()) + + type_name = doc.get("type") + if not isinstance(type_name, str): + raise ValueError("Model artifact missing string field 'type'") + + model_cls = _RL_MODEL_REGISTRY.get(type_name) + if model_cls is None: + known = ", ".join(sorted(_RL_MODEL_REGISTRY)) or "" + raise ValueError(f"Unknown RLModel type '{type_name}'. Known: {known}") + + payload = doc.get("payload") + # Backward compatibility: older artifacts stored fields at top-level. + if payload is None: + payload = {k: v for k, v in doc.items() if k not in ("type", "version")} + if not isinstance(payload, dict): + raise ValueError("Model artifact field 'payload' must be an object") + + return model_cls.from_payload(payload) diff --git a/src/brittle_star_project/rl/random_policy_model.py b/src/brittle_star_project/rl/random_policy_model.py new file mode 100644 index 0000000..9372d49 --- /dev/null +++ b/src/brittle_star_project/rl/random_policy_model.py @@ -0,0 +1,52 @@ +from __future__ import annotations + +from dataclasses import dataclass, field + +import numpy as np + +from .base import RLModel, register_rl_model + + +@register_rl_model("random") +@dataclass(slots=True) +class RandomPolicyModel(RLModel): + """A minimal, serializable policy model that outputs random controls. + + This is intentionally *not* a learning algorithm yet. It exists so we can: + - produce a stable model artifact from `train.py` + - load that artifact in `simulate.py` + - drive the MuJoCo viewer with the model's actions + """ + + nu: int = 0 + seed: int = 0 + ctrl_noise_scale: float = 0.5 + + _rng: np.random.RandomState = field(init=False, repr=False) + + def __post_init__(self) -> None: + self.reset(self.seed) + + def reset(self, seed: int | None = None) -> None: + if seed is not None: + self.seed = int(seed) + self._rng = np.random.RandomState(self.seed) + + def act(self, *, obs: np.ndarray | None = None, t: float = 0.0) -> np.ndarray: + if self.nu <= 0: + return np.zeros((0,), dtype=np.float32) + ctrl = self.ctrl_noise_scale * self._rng.randn(self.nu) + return ctrl.astype(np.float32) + + def to_payload(self) -> dict[str, object]: + return { + "seed": int(self.seed), + "ctrl_noise_scale": float(self.ctrl_noise_scale), + } + + @classmethod + def from_payload(cls, payload: dict[str, object]) -> RandomPolicyModel: + return cls( + seed=int(payload.get("seed", 0)), + ctrl_noise_scale=float(payload.get("ctrl_noise_scale", 0.5)), + ) diff --git a/src/train.py b/src/train.py new file mode 100644 index 0000000..eb64bbe --- /dev/null +++ b/src/train.py @@ -0,0 +1,377 @@ +import random +import time +from dataclasses import asdict +from functools import partial +from typing import Callable + +import flax +import jax +import jax.numpy as jnp +import numpy as np +import optax +import torch +import tqdm +import tyro +from flax.training.train_state import TrainState +from torch.utils.tensorboard import SummaryWriter + +from brittle_star_project.dataclasses import PPOArgs +from brittle_star_project.dataclasses.EpisodeStatistics import EpisodeStatistics +from brittle_star_project.environment.BrittleStarJaxEnvWrapper import BrittleStarJaxEnvWrapper +from brittle_star_project.rl import Network, Actor, Critic, AgentParams, Storage + + +def convert_obs_dict_to_array(obs_dict: dict) -> jnp.ndarray: + return jax.vmap(lambda o: jnp.concatenate([v.flatten() for v in o.values() if v.size > 0]))( + obs_dict + ) + + +def make_env(num_envs: int) -> Callable: + def thunk(): + return BrittleStarJaxEnvWrapper.default(num_envs=num_envs) + + return thunk + + +def train(args: PPOArgs): + args.batch_size = args.num_envs * args.num_steps + args.minibatch_size = args.batch_size // args.num_minibatches + args.num_iterations = args.total_timesteps // args.batch_size + run_name = f"{args.exp_name}__seed_{args.seed}__{int(time.time())}" + print(f"running name: {run_name}") + + 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, + save_code=True, + ) + + writer = SummaryWriter(f"runs/{run_name}") + writer.add_text( + "hyperparameters", + "|param|value|\n|---|---|\n" + "\n".join(f"|{k}|{v}|" for k, v in vars(args).items()), + ) + + random.seed(args.seed) + np.random.seed(args.seed) + key = jax.random.PRNGKey(args.seed) + key, network_key, actor_key, critic_key = jax.random.split(key, 4) + + torch.backends.cudnn.deterministic = args.torch_deterministic + device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu") + print(f"Running on device: {device}") + + print("Creating the environment...") + env = make_env(num_envs=args.num_envs)() + + episode_stats = EpisodeStatistics( + episode_returns=jnp.zeros(args.num_envs, dtype=jnp.float32), + episode_lengths=jnp.zeros(args.num_envs, dtype=jnp.int32), + returned_episode_returns=jnp.zeros(args.num_envs, jnp.float32), + returned_episode_lengths=jnp.zeros(args.num_envs, dtype=jnp.int32), + ) + + def step_env_wrapped(episode_stats: EpisodeStatistics, env_state, action): + next_env_state = env.step(env_state, action) + + # Extract per-environment signals from the state object + reward = next_env_state.reward # (num_envs,) + terminated = next_env_state.terminated # (num_envs,) + truncated = next_env_state.truncated # (num_envs,) + done = terminated | truncated # (num_envs,) + + new_episode_return = episode_stats.episode_returns + reward + new_episode_length = episode_stats.episode_lengths + 1 + + episode_stats = episode_stats.replace( + episode_returns=new_episode_return * (1 - done), + episode_lengths=new_episode_length * (1 - done), + returned_episode_returns=jnp.where( + done, new_episode_return, episode_stats.returned_episode_returns + ), + returned_episode_lengths=jnp.where( + done, new_episode_length, episode_stats.returned_episode_lengths + ), + ) + return ( + episode_stats, + next_env_state, + (convert_obs_dict_to_array(next_env_state.observations), reward, done), + ) + + def linear_schedule(count): + frac = 1.0 - (count // (args.num_minibatches * args.update_epochs)) / args.num_iterations + return args.learning_rate * frac + + print("Initializing the models...") + network = Network() + actor = Actor(action_dim=env.single_action_space.shape[0]) # continuous actions for MJX + critic = Critic() + + sample_obs = jnp.concatenate( + [ + v.flatten() + for v in env.single_observation_space.sample(rng=jax.random.PRNGKey(0)).values() + if v.size > 0 + ] + ) + network_params = network.init(network_key, sample_obs) + actor_params = actor.init(actor_key, network.apply(network_params, sample_obs)) + critic_params = critic.init(critic_key, network.apply(network_params, sample_obs)) + + agent_state = TrainState.create( + apply_fn=None, + params=asdict(AgentParams(network_params, actor_params, critic_params)), + tx=optax.chain( + optax.clip_by_global_norm(args.max_grad_norm), + optax.inject_hyperparams(optax.adam)( + learning_rate=linear_schedule if args.anneal_lr else args.learning_rate, eps=1e-5 + ), + ), + ) + + network.apply = jax.jit(network.apply) + actor.apply = jax.jit(actor.apply) + critic.apply = jax.jit(critic.apply) + + @jax.jit + def get_action_and_value_noise( + agent_state: TrainState, + next_obs: jnp.ndarray, + key: jax.random.PRNGKey, + ): + hidden = network.apply(agent_state.params["network_params"], next_obs) + # Continuous actions: sample from a Gaussian parameterized by the actor + mean, log_std = actor.apply(agent_state.params["actor_params"], hidden) + key, subkey = jax.random.split(key) + noise = jax.random.normal(subkey, shape=mean.shape) + std = jnp.exp(log_std) + action = mean + noise * std + logprob = -0.5 * (((action - mean) / std) ** 2 + 2 * log_std + jnp.log(2 * jnp.pi)).sum(-1) + value = critic.apply(agent_state.params["critic_params"], hidden) + return action, logprob, value.squeeze(-1), key + + @jax.jit + def get_action_and_value( + params: flax.core.FrozenDict, + x: jnp.ndarray, + action: np.ndarray, + ): + hidden = network.apply(params["network_params"], x) + mean, log_std = actor.apply(params["actor_params"], hidden) + std = jnp.exp(log_std) + logprob = -0.5 * (((action - mean) / std) ** 2 + 2 * log_std + jnp.log(2 * jnp.pi)).sum(-1) + entropy = (0.5 + 0.5 * jnp.log(2 * jnp.pi) + log_std).sum(-1) + value = critic.apply(params["critic_params"], hidden).squeeze(-1) + return logprob, entropy, value + + @jax.jit + def compute_gae_once(carry, inp, gamma, gae_lambda): + advantages = carry + nextdone, nextvalues, curvalues, reward = inp + nextnonterminal = 1.0 - nextdone + delta = reward + gamma * nextvalues * nextnonterminal - curvalues + advantages = delta + gamma * gae_lambda * nextnonterminal * advantages + return advantages, advantages + + @jax.jit + def compute_gae(agent_state, next_obs, next_done, storage): + next_value = critic.apply( + agent_state.params["critic_params"], + network.apply(agent_state.params["network_params"], next_obs), + ).squeeze(-1) + + advantages = jnp.zeros((args.num_envs,)) + dones = jnp.concatenate([storage.dones, next_done[None, :]], axis=0) + values = jnp.concatenate([storage.values, next_value[None, :]], axis=0) + _, advantages = jax.lax.scan( + partial(compute_gae_once, gamma=args.gamma, gae_lambda=args.gae_lambda), + advantages, + (dones[1:], values[1:], values[:-1], storage.rewards), + reverse=True, + ) + return storage.replace(advantages=advantages, returns=advantages + storage.values) + + def ppo_loss(params, x, a, logp, mb_advantages, mb_returns): + newlogprob, entropy, newvalue = get_action_and_value(params, x, a) + logratio = newlogprob - logp + ratio = jnp.exp(logratio) + approx_kl = ((ratio - 1) - logratio).mean() + + if args.norm_adv: + mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8) + + pg_loss1 = -mb_advantages * ratio + pg_loss2 = -mb_advantages * jnp.clip(ratio, 1 - args.clip_coef, 1 + args.clip_coef) + pg_loss = jnp.maximum(pg_loss1, pg_loss2).mean() + v_loss = 0.5 * ((newvalue - mb_returns) ** 2).mean() + entropy_loss = entropy.mean() + loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef + return loss, (pg_loss, v_loss, entropy_loss, jax.lax.stop_gradient(approx_kl)) + + ppo_loss_grad_fn = jax.value_and_grad(ppo_loss, has_aux=True) + + @jax.jit + def update_ppo(agent_state, storage, key): + def update_epoch(carry, _): + agent_state, key = carry + key, subkey = jax.random.split(key) + + def flatten(x): + return x.reshape((-1,) + x.shape[2:]) + + def convert_data(x): + x = jax.random.permutation(subkey, x) + return jnp.reshape(x, (args.num_minibatches, -1) + x.shape[1:]) + + flatten_storage = jax.tree.map(flatten, storage) + shuffled_storage = jax.tree.map(convert_data, flatten_storage) + + def update_minibatch(agent_state, minibatch): + (loss, (pg_loss, v_loss, entropy_loss, approx_kl)), grads = ppo_loss_grad_fn( + agent_state.params, + minibatch.obs, + minibatch.actions, + minibatch.logprobs, + minibatch.advantages, + minibatch.returns, + ) + agent_state = agent_state.apply_gradients(grads=grads) + return agent_state, (loss, pg_loss, v_loss, entropy_loss, approx_kl, grads) + + agent_state, metrics = jax.lax.scan(update_minibatch, agent_state, shuffled_storage) + return (agent_state, key), metrics + + (agent_state, key), (loss, pg_loss, v_loss, entropy_loss, approx_kl, grads) = jax.lax.scan( + update_epoch, (agent_state, key), (), length=args.update_epochs + ) + return agent_state, loss, pg_loss, v_loss, entropy_loss, approx_kl, key + + # --- Main training loop --- + global_step = 0 + start_time = time.time() + + # Reset once to get initial state + print("Resetting the environment...") + next_env_state = env.reset(seed=args.seed) + next_obs = convert_obs_dict_to_array(next_env_state.observations) + next_done = jnp.zeros(args.num_envs, dtype=jnp.bool_) + + def step_once(carry, _, env_step_fn): + agent_state, episode_stats, obs, done, key, env_state = carry + action, logprob, value, key = get_action_and_value_noise(agent_state, obs, key) + + episode_stats, env_state, (next_obs, reward, next_done) = env_step_fn( + episode_stats, env_state, action + ) + + storage = Storage( + obs=obs, + actions=action, + logprobs=logprob, + dones=done, + values=value, + rewards=reward, + returns=jnp.zeros_like(reward), + advantages=jnp.zeros_like(reward), + ) + return (agent_state, episode_stats, next_obs, next_done, key, env_state), storage + + def rollout( + agent_state, episode_stats, next_obs, next_done, key, env_state, step_once_fn, max_steps + ): + (agent_state, episode_stats, next_obs, next_done, key, env_state), storage = jax.lax.scan( + step_once_fn, + (agent_state, episode_stats, next_obs, next_done, key, env_state), + (), + max_steps, + ) + return agent_state, episode_stats, next_obs, next_done, storage, key, env_state + + rollout = partial( + rollout, + step_once_fn=partial(step_once, env_step_fn=step_env_wrapped), + max_steps=args.num_steps, + ) + + print("Starting training...") + iters_bar = tqdm.tqdm(range(1, args.num_iterations + 1)) + for _ in iters_bar: + iteration_time_start = time.time() + + agent_state, episode_stats, next_obs, next_done, storage, key, next_env_state = rollout( + agent_state, episode_stats, next_obs, next_done, key, next_env_state + ) + + global_step += args.num_steps * args.num_envs + storage = compute_gae(agent_state, next_obs, next_done, storage) + agent_state, loss, pg_loss, v_loss, entropy_loss, approx_kl, key = update_ppo( + agent_state, storage, key + ) + + avg_episodic_return = np.mean(jax.device_get(episode_stats.returned_episode_returns)) + iters_bar.set_postfix_str( + f"global_step={global_step}, avg_episodic_return={avg_episodic_return}" + ) + + writer.add_scalar("charts/avg_episodic_return", avg_episodic_return, global_step) + writer.add_scalar( + "charts/avg_episodic_length", + np.mean(jax.device_get(episode_stats.returned_episode_lengths)), + global_step, + ) + writer.add_scalar( + "charts/learning_rate", + agent_state.opt_state[1].hyperparams["learning_rate"].item(), + global_step, + ) + writer.add_scalar("losses/value_loss", v_loss[-1, -1].item(), global_step) + writer.add_scalar("losses/policy_loss", pg_loss[-1, -1].item(), global_step) + writer.add_scalar("losses/entropy", entropy_loss[-1, -1].item(), global_step) + writer.add_scalar("losses/approx_kl", approx_kl[-1, -1].item(), global_step) + writer.add_scalar("losses/loss", loss[-1, -1].item(), global_step) + + # iters_bar.set_postfix_str(f"SPS: {int(global_step / (time.time() - start_time))}") + + writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step) + writer.add_scalar( + "charts/SPS_update", + int(args.num_envs * args.num_steps / (time.time() - iteration_time_start)), + global_step, + ) + + if args.save_model: + model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model" + with open(model_path, "wb") as f: + f.write( + flax.serialization.to_bytes( + [ + vars(args), + [ + agent_state.params["network_params"], + agent_state.params["actor_params"], + agent_state.params["critic_params"], + ], + ] + ) + ) + print(f"model saved to {model_path}") + + env.close() + writer.close() + + +def main() -> None: + args = tyro.cli(PPOArgs) + train(args) + + +if __name__ == "__main__": + main() diff --git a/uv.lock 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