Environment setup + train loop (#5)

Mujoco environment setup (vectorized on GPU) + training loop + simulate script

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()