Merge pull request #25 from Nam-dada/main

Add mujoco_playground problem and example test

examples/mujoco_playground/swimmer6.py

@@ -0,0 +1,61 @@
+from tensorneat.pipeline import Pipeline
+from tensorneat.algorithm.neat import NEAT
+from tensorneat.genome import DefaultGenome, BiasNode, DefaultConn, DefaultMutation
+
+from tensorneat.problem.rl import MujocoEnv
+from tensorneat.common import ACT, AGG
+import jax
+
+
+def random_sample_policy(randkey, obs):
+    return jax.random.uniform(randkey, (8,), minval=-1.0, maxval=1.0)
+
+
+if __name__ == "__main__":
+    pipeline = Pipeline(
+        algorithm=NEAT(
+            pop_size=3000,
+            species_size=20,
+            survival_threshold=0.1,
+            compatibility_threshold=0.8,
+            genome=DefaultGenome(
+                max_nodes=100,
+                max_conns=1500,
+                num_inputs=25,
+                num_outputs=5,
+                init_hidden_layers=(30,),
+                mutation=DefaultMutation(
+                    node_delete=0.0,
+                ),
+                node_gene=BiasNode(
+                    bias_init_std=0.1,
+                    bias_mutate_power=0.05,
+                    bias_mutate_rate=0.01,
+                    bias_replace_rate=0.0,
+                    activation_options=ACT.tanh,
+                    aggregation_options=AGG.sum,
+                ),
+                conn_gene=DefaultConn(
+                    weight_init_mean=0.0,
+                    weight_init_std=0.1,
+                    weight_mutate_power=0.05,
+                    weight_replace_rate=0.0,
+                    weight_mutate_rate=0.001,
+                ),
+                output_transform=ACT.tanh,
+            ),
+        ),
+        problem=MujocoEnv(
+            env_name="SwimmerSwimmer6",
+            max_step=1000,
+        ),
+        seed=42,
+        generation_limit=100,
+        fitness_target=8000,
+    )
+
+    # initialize state
+    state = pipeline.setup()
+    # print(state)
+    # run until terminate
+    state, best = pipeline.auto_run(state)

src/tensorneat/problem/rl/__init__.py

@@ -1,3 +1,4 @@
 from .gymnax import GymNaxEnv
 from .brax import BraxEnv
 from .rl_jit import RLEnv
+from .mujoco_playground import MujocoEnv

src/tensorneat/problem/rl/mujoco_playground.py

@@ -0,0 +1,119 @@
+import jax.numpy as jnp
+from jax import Array
+from mujoco_playground import registry
+
+from .rl_jit import RLEnv, norm_obs
+
+
+class MujocoEnv(RLEnv):
+    def __init__(
+        self, env_name: str = "SwimmerSwimmer6", *args, **kwargs
+    ):
+        super().__init__(*args, **kwargs)
+        self.env_name = env_name
+        self.env = registry.load(env_name=env_name)
+
+    def env_step(self, randkey, env_state, action):
+        state = self.env.step(env_state, action)
+        obs = state.obs
+        if not isinstance(obs, Array):
+            if "state" in obs:
+                obs = obs["state"]
+            else:
+                raise ImportError(
+                    f"This Pytree observation space is not supported yet: {obs}"
+                )
+        return obs, state, state.reward, state.done.astype(jnp.bool_), state.info
+
+    def env_reset(self, randkey):
+        init_state = self.env.reset(randkey)
+        obs = init_state.obs
+        if not isinstance(obs, Array):
+            if "state" in obs:
+                obs = obs["state"]
+            else:
+                raise ImportError(
+                    f"This Pytree observation space is not supported yet: {obs}"
+                )
+        return obs, init_state
+
+    @property
+    def input_shape(self):
+        return (self.env.observation_size,)
+
+    @property
+    def output_shape(self):
+        return (self.env.action_size,)
+
+    def show(
+            self,
+            state,
+            randkey,
+            act_func,
+            params,
+            save_path=None,
+            height=480,
+            width=480,
+            output_type="rgb_array",
+            *args,
+            **kwargs,
+        ):
+
+        assert output_type in ["gif", "mp4"]
+
+        import jax
+        import imageio
+        from brax.io import image
+        import numpy as np
+
+        obs, env_state = self.reset(randkey)
+        reward, done = 0.0, False
+        state_histories = [env_state.pipeline_state]
+
+        def step(key, env_state, obs):
+            key, _ = jax.random.split(key)
+
+            if self.obs_normalization:
+                obs = norm_obs(state, obs)
+
+            if self.action_policy is not None:
+                forward_func = lambda obs: act_func(state, params, obs)
+                action = self.action_policy(key, forward_func, obs)
+            else:
+                action = act_func(state, params, obs)
+
+            obs, env_state, r, done, info = self.step(randkey, env_state, action)
+            return key, env_state, obs, r, done
+
+        jit_step = jax.jit(step)
+
+        for _ in range(self.max_step):
+            key, env_state, obs, r, done = jit_step(randkey, env_state, obs)
+            state_histories.append(env_state.pipeline_state)
+            reward += r
+            if done:
+                break
+
+        print("Total reward: ", reward)
+
+        try:
+            imgs = image.render_array(
+                sys=self.env.sys, trajectory=state_histories, height=height, width=width, camera="track"
+            )
+        except ValueError:
+            imgs = image.render_array(
+                sys=self.env.sys, trajectory=state_histories, height=height, width=width
+            )
+
+        if save_path is None:
+            save_path = f"{self.env_name}.{output_type}"
+
+        imageio.mimsave(save_path, imgs, *args, **kwargs)
+
+        if output_type == "gif":
+            imageio.mimsave(save_path, imgs, *args, **kwargs)
+        elif output_type == "mp4":
+            fps = kwargs.get("fps", 30)
+            imageio.mimsave(save_path, imgs, fps=fps, codec="libx264", format="mp4")
+
+        print(f"{output_type} saved to: ", save_path)