test

src/tensorneat/mjx_swimmer.py

@@ -0,0 +1,58 @@
+
+from tensorneat.pipeline import Pipeline
+from tensorneat.algorithm.neat import NEAT
+from tensorneat.genome import DefaultGenome, BiasNode, DefaultConn,DefaultMutation
+
+from 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 .rl_jit import RLEnv
+from .mujoco_playground import MujocoEnv