odify genome for the official release

examples/gymnax/cartpole_hyperneat.py

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+import jax
+
+from pipeline import Pipeline
+from algorithm.neat import *
+from algorithm.hyperneat import *
+from tensorneat.common import Act
+
+from problem.rl_env import GymNaxEnv
+
+if __name__ == "__main__":
+    pipeline = Pipeline(
+        algorithm=HyperNEAT(
+            substrate=FullSubstrate(
+                input_coors=[
+                    (-1, -1),
+                    (-0.5, -1),
+                    (0, -1),
+                    (0.5, -1),
+                    (1, -1),
+                ],  # 4(problem inputs) + 1(bias)
+                hidden_coors=[
+                    (-1, -0.5),
+                    (0.333, -0.5),
+                    (-0.333, -0.5),
+                    (1, -0.5),
+                    (-1, 0),
+                    (0.333, 0),
+                    (-0.333, 0),
+                    (1, 0),
+                    (-1, 0.5),
+                    (0.333, 0.5),
+                    (-0.333, 0.5),
+                    (1, 0.5),
+                ],
+                output_coors=[
+                    (-1, 1),
+                    (1, 1),  # one output
+                ],
+            ),
+            neat=NEAT(
+                species=DefaultSpecies(
+                    genome=DefaultGenome(
+                        num_inputs=4,  # [*coor1, *coor2]
+                        num_outputs=1,  # the weight of connection between two coor1 and coor2
+                        max_nodes=50,
+                        max_conns=100,
+                        node_gene=DefaultNodeGene(
+                            activation_default=Act.tanh,
+                            activation_options=(Act.tanh,),
+                        ),
+                        output_transform=Act.tanh,  # the activation function for output node in NEAT
+                    ),
+                    pop_size=10000,
+                    species_size=10,
+                    compatibility_threshold=3.5,
+                    survival_threshold=0.03,
+                ),
+            ),
+            activation=Act.tanh,  # the activation function for output node in HyperNEAT
+            activate_time=10,
+            output_transform=jax.numpy.argmax,  # action of cartpole is in {0, 1}
+        ),
+        problem=GymNaxEnv(
+            env_name="CartPole-v1",
+        ),
+        generation_limit=300,
+        fitness_target=500,
+    )
+
+    # initialize state
+    state = pipeline.setup()
+    # print(state)
+    # run until terminate
+    state, best = pipeline.auto_run(state)