52 lines
1.4 KiB
Python
52 lines
1.4 KiB
Python
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 BraxEnv
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from tensorneat.common import ACT, AGG
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import jax
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def random_sample_policy(randkey, obs):
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return jax.random.uniform(randkey, (6,), minval=-1.0, maxval=1.0)
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if __name__ == "__main__":
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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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max_nodes=100,
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max_conns=200,
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num_inputs=17,
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num_outputs=6,
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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=ACT.standard_tanh,
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),
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),
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problem=BraxEnv(
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env_name="halfcheetah",
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max_step=1000,
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obs_normalization=True,
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sample_episodes=1000,
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sample_policy=random_sample_policy,
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),
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seed=42,
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generation_limit=100,
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fitness_target=8000,
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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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