50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
import jax.numpy as jnp
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from tensorneat.pipeline import Pipeline
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from tensorneat.algorithm.neat import NEAT
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from tensorneat.algorithm.hyperneat import HyperNEAT, FullSubstrate
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from tensorneat.genome import DefaultGenome
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from tensorneat.common import Act
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from tensorneat.problem import GymNaxEnv
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if __name__ == "__main__":
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# the num of input_coors is 5
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# 4 is for cartpole inputs, 1 is for bias
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pipeline = Pipeline(
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algorithm=HyperNEAT(
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substrate=FullSubstrate(
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input_coors=((-1, -1), (-0.5, -1), (0, -1), (0.5, -1), (1, -1)),
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hidden_coors=((-1, 0), (0, 0), (1, 0)),
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output_coors=((-1, 1), (1, 1)),
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),
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neat=NEAT(
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pop_size=10000,
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species_size=20,
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survival_threshold=0.01,
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genome=DefaultGenome(
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num_inputs=4, # size of query coors
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num_outputs=1,
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init_hidden_layers=(),
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output_transform=Act.standard_tanh,
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),
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),
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activation=Act.tanh,
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activate_time=10,
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output_transform=jnp.argmax,
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),
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problem=GymNaxEnv(
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env_name="CartPole-v1",
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repeat_times=5,
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),
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generation_limit=300,
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fitness_target=-1e-6,
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)
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# initialize state
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state = pipeline.setup()
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# print(state)
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# run until terminate
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state, best = pipeline.auto_run(state)
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