import gym from testing import test_actor_class from actor import ModifiedGeneticNNActor as MGNNA from genetics import top_selection if __name__ == '__main__': env = gym.make('HalfCheetah-v2') print("Observation space's shape: ", str(env.observation_space.low.shape)) print("Action space's shape: ", str(env.action_space.low.shape)) test_actor_class( MGNNA, env, savefile='HalfCheetah-v2-GNNAM.txt', population_size=100, actor_args={'hidden_layers': [3, 3]}, evolve_args={ 'generations': 101, 'simulation_reps': 5, # Changed 'max_steps': 1000, 'selection': lambda p: top_selection(p, cutoff=0.20), 'keep_parents_alive': True, 'p_mutation': 0.2, 'mutation_scale': 0.5, 'render_gens': 20, 'savenum': 1, 'allow_parallel': True }, render_args={ 'fps': 60, 'max_steps': 3000 })
import gym from testing import test_actor_class from actor import ModifiedGeneticNNActor as MGNNA from genetics import top_selection if __name__ == '__main__': env = gym.make('HalfCheetah-v2') print("Observation space's shape: ", str(env.observation_space.low.shape)) print("Action space's shape: ", str(env.action_space.low.shape)) test_actor_class( MGNNA, env, savefile='HalfCheetah-v2-GNNAM.txt', population_size=100, actor_args={'hidden_layers': [3, 3]}, evolve_args={ 'generations': 101, 'simulation_reps': 1, 'max_steps': 1000, 'selection': lambda p: top_selection(p, cutoff=0.60), # Changed 'keep_parents_alive': True, 'p_mutation': 0.2, 'mutation_scale': 0.5, 'render_gens': 20, 'savenum': 1, 'allow_parallel': True }, render_args={ 'fps': 60, 'max_steps': 3000 })