38 lines
1.0 KiB
Python
38 lines
1.0 KiB
Python
from pipeline import Pipeline
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from algorithm.neat import *
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from problem.rl_env import BraxEnv
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from tensorneat.common import Act
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if __name__ == "__main__":
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pipeline = Pipeline(
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algorithm=NEAT(
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species=DefaultSpecies(
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genome=DefaultGenome(
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num_inputs=11,
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num_outputs=2,
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max_nodes=50,
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max_conns=100,
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node_gene=DefaultNodeGene(
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activation_options=(Act.tanh,),
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activation_default=Act.tanh,
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),
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output_transform=Act.tanh,
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),
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pop_size=100,
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species_size=10,
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),
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),
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problem=BraxEnv(
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env_name="reacher",
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),
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generation_limit=10000,
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fitness_target=5000,
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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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