ee1a2a8271
1. Add origin_node and origin_conn. 2. Change the behavior of crossover and mutation. Now, TensorNEAT will use all fix_attrs(include historical marker if it has one) as identifier for gene in crossover and distance calculation. 3. Other slightly change. 4. Add two related examples: xor_origin and hopper_origin 5. Add related test file.
50 lines
1.5 KiB
Python
50 lines
1.5 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, OriginNode, OriginConn
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from tensorneat.problem.rl import BraxEnv
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from tensorneat.common import ACT, AGG
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"""
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Solving Hopper with OriginGene
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See https://github.com/EMI-Group/tensorneat/issues/11
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"""
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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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num_inputs=11,
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num_outputs=3,
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init_hidden_layers=(),
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# origin node gene, which use the same crossover behavior to the origin NEAT paper
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node_gene=OriginNode(
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activation_options=ACT.tanh,
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aggregation_options=AGG.sum,
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response_lower_bound = 1,
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response_upper_bound= 1, # fix response to 1
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),
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# use origin connection, which using historical marker
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conn_gene=OriginConn(),
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output_transform=ACT.tanh,
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),
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),
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problem=BraxEnv(
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env_name="hopper",
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max_step=1000,
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),
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seed=42,
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generation_limit=100,
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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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