update some examples

examples/gymnax/mountain_car_continuous.py

@@ -1,37 +1,43 @@
-from pipeline import Pipeline
-from algorithm.neat import *
+import jax.numpy as jnp
+
+from tensorneat.pipeline import Pipeline
+from tensorneat.algorithm.neat import NEAT
+from tensorneat.genome import DefaultGenome, BiasNode
+
+from tensorneat.problem.rl import GymNaxEnv
+from tensorneat.common import Act, Agg
+
 
-from problem.rl_env import GymNaxEnv
-from tensorneat.common import Act
 
 if __name__ == "__main__":
     pipeline = Pipeline(
         algorithm=NEAT(
-            species=DefaultSpecies(
-                genome=DefaultGenome(
-                    num_inputs=2,
-                    num_outputs=1,
-                    max_nodes=50,
-                    max_conns=100,
-                    node_gene=DefaultNodeGene(
-                        activation_options=(Act.tanh,),
-                        activation_default=Act.tanh,
-                    ),
-                    output_transform=Act.tanh
+            pop_size=1000,
+            species_size=20,
+            survival_threshold=0.1,
+            compatibility_threshold=1.0,
+            genome=DefaultGenome(
+                num_inputs=2,
+                num_outputs=1,
+                init_hidden_layers=(),
+                node_gene=BiasNode(
+                    activation_options=Act.tanh,
+                    aggregation_options=Agg.sum,
                 ),
-                pop_size=10000,
-                species_size=10,
+                output_transform=Act.standard_tanh,
             ),
         ),
         problem=GymNaxEnv(
             env_name="MountainCarContinuous-v0",
+            repeat_times=5,
         ),
-        generation_limit=10000,
+        seed=42,
+        generation_limit=100,
         fitness_target=99,
     )
 
     # initialize state
     state = pipeline.setup()
-    # print(state)
+
     # run until terminate
     state, best = pipeline.auto_run(state)