46 lines
1.2 KiB
Python
46 lines
1.2 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.genome import DefaultGenome, BiasNode
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from tensorneat.problem.rl import GymNaxEnv
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from tensorneat.common import Act, Agg
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if __name__ == "__main__":
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# the network has 3 outputs, the max one will be the action
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# as the action of acrobot is {0, 1, 2}
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pipeline = Pipeline(
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algorithm=NEAT(
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pop_size=1000,
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species_size=20,
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survival_threshold=0.1,
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compatibility_threshold=1.0,
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genome=DefaultGenome(
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num_inputs=6,
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num_outputs=3,
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init_hidden_layers=(),
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node_gene=BiasNode(
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activation_options=Act.tanh,
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aggregation_options=Agg.sum,
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),
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output_transform=jnp.argmax,
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),
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),
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problem=GymNaxEnv(
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env_name="Acrobot-v1",
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),
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seed=42,
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generation_limit=100,
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fitness_target=-60,
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)
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# initialize state
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state = pipeline.setup()
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# run until terminate
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state, best = pipeline.auto_run(state)
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