update some examples

examples/brax/halfcheetah.py

@@ -0,0 +1,51 @@
+from tensorneat.pipeline import Pipeline
+from tensorneat.algorithm.neat import NEAT
+from tensorneat.genome import DefaultGenome, BiasNode
+
+from tensorneat.problem.rl import BraxEnv
+from tensorneat.common import Act, Agg
+
+import jax
+
+
+def random_sample_policy(randkey, obs):
+    return jax.random.uniform(randkey, (6,), minval=-1.0, maxval=1.0)
+
+
+if __name__ == "__main__":
+    pipeline = Pipeline(
+        algorithm=NEAT(
+            pop_size=1000,
+            species_size=20,
+            survival_threshold=0.1,
+            compatibility_threshold=1.0,
+            genome=DefaultGenome(
+                max_nodes=100,
+                max_conns=200,
+                num_inputs=17,
+                num_outputs=6,
+                init_hidden_layers=(),
+                node_gene=BiasNode(
+                    activation_options=Act.tanh,
+                    aggregation_options=Agg.sum,
+                ),
+                output_transform=Act.standard_tanh,
+            ),
+        ),
+        problem=BraxEnv(
+            env_name="halfcheetah",
+            max_step=1000,
+            obs_normalization=True,
+            sample_episodes=1000,
+            sample_policy=random_sample_policy,
+        ),
+        seed=42,
+        generation_limit=100,
+        fitness_target=8000,
+    )
+
+    # initialize state
+    state = pipeline.setup()
+    # print(state)
+    # run until terminate
+    state, best = pipeline.auto_run(state)