Morphology/brittle star 2 arms (#17)

Provide the features to read in Environment (Morphology, arena, ...) config files in the JSON format. The example JSON file contains the config for a brittle star with 2 arms

configs/example.json

@@ -0,0 +1,8 @@
+{
+  "morphology": {
+    "num_arms": 2,
+    "num_segments_per_arm": 4,
+    "use_p_control": true,
+    "use_torque_control": false
+  }
+}

experiments/simulate.py

@@ -4,17 +4,15 @@ import argparse
 from pathlib import Path
 
 from brittle_star_project import (
-    ArenaConfig,
     Backend,
     BrittleStarEnv,
     BrittleStarEnvFactory,
-    EnvConfig,
+    SimulationConfig,
-    MorphologyConfig,
+    simulate_policy,
-    Task,
 )
+from brittle_star_project.environment import from_json
 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)
@@ -35,9 +33,9 @@ def parse_args() -> argparse.Namespace:
         help="Which model class to instantiate when --model is omitted.",
     )
     p.add_argument(
-        "--task",
+        "--backend",
-        choices=[t.value for t in Task],
+        choices=[b for b in Backend],
-        default=Task.DIRECTED_LOCOMOTION.value,
+        default=Backend.MJX,
     )
     p.add_argument("--seed", type=int, default=None)
     return p.parse_args()
@@ -46,14 +44,11 @@ def parse_args() -> argparse.Namespace:
 def main() -> None:
     args = parse_args()
 
+    morphology_cfg, arena_cfg, env_cfg = from_json("../configs/test.json")
+
     # ======= ENVIRONMENT SETUP =======
 
-    backend = Backend.MJC
+    backend = args.backend
-    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)

ruff.toml

@@ -393,4 +393,3 @@ extend-ignore = [
   "PLW0603",  # global-statement
   # "PLW1404",  # implicit-str-concat
 ]
-

src/__init__.py

@@ -0,0 +1,25 @@
+from .brittle_star_project import (
+    ArenaConfig,
+    Backend,
+    BrittleStarEnv,
+    BrittleStarEnvFactory,
+    EnvConfig,
+    MorphologyConfig,
+    Task,
+    simulate_policy,
+    SimulationConfig,
+    ControlPolicy,
+)
+
+__all__ = [
+    "ArenaConfig",
+    "Backend",
+    "BrittleStarEnv",
+    "BrittleStarEnvFactory",
+    "EnvConfig",
+    "MorphologyConfig",
+    "Task",
+    "simulate_policy",
+    "SimulationConfig",
+    "ControlPolicy",
+]

src/brittle_star_project/__init__.py

@@ -2,6 +2,7 @@ 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
+from .render import simulate_policy, SimulationConfig, ControlPolicy
 
 __all__ = [
     "ArenaConfig",
@@ -11,4 +12,7 @@ __all__ = [
     "EnvConfig",
     "MorphologyConfig",
     "Task",
+    "simulate_policy",
+    "SimulationConfig",
+    "ControlPolicy",
 ]

src/brittle_star_project/dataclasses/PPOArgs.py

@@ -7,6 +7,9 @@ class PPOArgs:
     source: https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_xla_jax_scan.py
     """
 
+    # path to environment config file, if None, use default config
+    config_path: str | None = None
+
     # the name of this experiment
     exp_name: str = "brittle_star_ppo"
 
@@ -41,8 +44,6 @@ class PPOArgs:
     hf_entity: str = ""
 
     # ==== Algorithm specific dataclasses ====
-    # the id of the environment
-    env_id: str = ""  # todo
 
     # total timesteps of the experiments
     total_timesteps: int = 10000000
@@ -51,7 +52,7 @@ class PPOArgs:
     learning_rate: float = 2.5e-4
 
     # the number of parallel game environments
-    num_envs: int = 16
+    num_envs: int = 100
 
     # the number of steps to run in each environment per policy rollout
     num_steps: int = 128

src/brittle_star_project/environment/BrittleStarJaxEnvWrapper.py

@@ -8,6 +8,7 @@ from brittle_star_project import (
     ArenaConfig,
     Backend,
 )
+from brittle_star_project.environment import from_json
 
 
 class BrittleStarJaxEnvWrapper:
@@ -76,3 +77,21 @@ class BrittleStarJaxEnvWrapper:
         return BrittleStarJaxEnvWrapper(
             morphology, arena, env_config, num_envs=num_envs, backend=backend
         )
+
+    @staticmethod
+    def from_config(
+        config_path: str, num_envs: int, backend: Backend = Backend.MJX
+    ) -> "BrittleStarJaxEnvWrapper":
+        morphology_cfg, arena_cfg, env_cfg = from_json(config_path)
+        return BrittleStarJaxEnvWrapper(
+            morphology_cfg, arena_cfg, env_cfg, num_envs=num_envs, backend=backend
+        )
+
+    def __str__(self):
+        morphology_str = str(self._morphology)
+        arena_str = str(self._arena)
+        env_config_str = str(self._env_config)
+        return (
+            f"BrittleStarJaxEnvWrapper(backend={self._backend}, num_envs={self._num_envs}, "
+            + f"morphology={morphology_str}, arena={arena_str}, env_config={env_config_str})"
+        )

src/brittle_star_project/environment/__init__.py

@@ -1,4 +1,4 @@
-from .env_config import ArenaConfig, EnvConfig, MorphologyConfig
+from .env_config import ArenaConfig, EnvConfig, MorphologyConfig, from_json
 from .env_types import Backend, Task
 from .env_wrapper import BrittleStarEnv, StepResult
 from .factory import BrittleStarEnvFactory
@@ -12,4 +12,5 @@ __all__ = [
     "BrittleStarEnv",
     "StepResult",
     "BrittleStarEnvFactory",
+    "from_json",
 ]

src/brittle_star_project/environment/env_config.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, field
+import json
 
 from .env_types import Task
 
@@ -48,6 +49,11 @@ class EnvConfig:
     # Per docs in upstream env config: integer factors of 200.
     light_perlin_noise_scale: int = 0
 
-    @staticmethod
+
-    def from_json(path: str) -> EnvConfig:
+def from_json(path: str) -> tuple[MorphologyConfig, ArenaConfig, EnvConfig]:
-        pass
+    with open(path, "r") as f:
+        config_json = json.load(f)
+        morphology = MorphologyConfig(**config_json.get("morphology", {}))
+        arena = ArenaConfig(**config_json.get("arena", {}))
+        env = EnvConfig(**config_json.get("env", {}))
+        return morphology, arena, env

src/brittle_star_project/render/__init__.py

@@ -0,0 +1,3 @@
+from .renderer import simulate_policy, SimulationConfig, ControlPolicy
+
+__all__ = ["simulate_policy", "SimulationConfig", "ControlPolicy"]

src/brittle_star_project/rl/DummyAgent.py

@@ -52,6 +52,7 @@ class AgentParams:
     network_params: flax.core.FrozenDict
     actor_params: flax.core.FrozenDict
     critic_params: flax.core.FrozenDict
+    critic_network_params: flax.core.FrozenDict
 
 
 @jax.tree_util.register_dataclass

src/train.py

@@ -7,6 +7,7 @@ from typing import Callable
 import flax
 import jax
 import jax.numpy as jnp
+import matplotlib.pyplot as plt
 import numpy as np
 import optax
 import torch
@@ -18,7 +19,8 @@ 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
+from brittle_star_project.rl import Actor, AgentParams, Critic, Network, Storage
+from ppo import PPO
 
 
 def convert_obs_dict_to_array(obs_dict: dict) -> jnp.ndarray:
@@ -27,9 +29,11 @@ def convert_obs_dict_to_array(obs_dict: dict) -> jnp.ndarray:
     )
 
 
-def make_env(num_envs: int) -> Callable:
+def make_env(config_path: str | None, num_envs: int) -> Callable:
     def thunk():
+        if config_path is None:
             return BrittleStarJaxEnvWrapper.default(num_envs=num_envs)
+        return BrittleStarJaxEnvWrapper.from_config(config_path, num_envs=num_envs)
 
     return thunk
 
@@ -62,14 +66,15 @@ def train(args: PPOArgs):
     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)
+    key, network_key, actor_key, critic_key, critic_network_key = jax.random.split(key, 5)
 
     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)()
+    env = make_env(config_path=args.config_path, num_envs=args.num_envs)()
+    print(f"Environment: {env}")
 
     episode_stats = EpisodeStatistics(
         episode_returns=jnp.zeros(args.num_envs, dtype=jnp.float32),
@@ -112,6 +117,7 @@ def train(args: PPOArgs):
 
     print("Initializing the models...")
     network = Network()
+    critic_network = Network()
     actor = Actor(action_dim=env.single_action_space.shape[0])  # continuous actions for MJX
     critic = Critic()
 
@@ -123,12 +129,15 @@ def train(args: PPOArgs):
         ]
     )
     network_params = network.init(network_key, sample_obs)
+    critic_network_params = critic_network.init(critic_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))
+    critic_params = critic.init(critic_key, critic_network.apply(critic_network_params, sample_obs))
 
     agent_state = TrainState.create(
         apply_fn=None,
-        params=asdict(AgentParams(network_params, actor_params, critic_params)),
+        params=asdict(
+            AgentParams(network_params, actor_params, critic_params, critic_network_params)
+        ),
         tx=optax.chain(
             optax.clip_by_global_norm(args.max_grad_norm),
             optax.inject_hyperparams(optax.adam)(
@@ -138,8 +147,10 @@ def train(args: PPOArgs):
     )
 
     network.apply = jax.jit(network.apply)
+    critic_network.apply = jax.jit(critic_network.apply)
     actor.apply = jax.jit(actor.apply)
     critic.apply = jax.jit(critic.apply)
+    ppo_instance = PPO(args, network, actor, critic, critic_network)
 
     @jax.jit
     def get_action_and_value_noise(
@@ -158,20 +169,6 @@ def train(args: PPOArgs):
         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
@@ -199,61 +196,6 @@ def train(args: PPOArgs):
         )
         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()
@@ -303,6 +245,7 @@ def train(args: PPOArgs):
 
     print("Starting training...")
     iters_bar = tqdm.tqdm(range(1, args.num_iterations + 1))
+    losses = []
     for _ in iters_bar:
         iteration_time_start = time.time()
 
@@ -312,10 +255,12 @@ def train(args: PPOArgs):
 
         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, loss, pg_loss, v_loss, entropy_loss, approx_kl, key = ppo_instance.update_ppo(
             agent_state, storage, key
         )
 
+        losses.append(jnp.mean(loss))
+
         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}"
@@ -367,6 +312,12 @@ def train(args: PPOArgs):
     env.close()
     writer.close()
 
+    print("Saving loss plot...")
+    plt.plot(losses)
+    plt.title("PPO Loss, mean over minibatches")
+    plt.savefig(f"runs/{run_name}/{args.exp_name}_losses.png")
+    plt.close()
+
 
 def main() -> None:
     args = tyro.cli(PPOArgs)