import gym from testing import test_actor_class from actor import GeneticPerceptronActor as GPA if __name__ == '__main__': env = gym.make('DuplicatedInput-v0') test_actor_class(GPA, env, savefile='DuplicatedInput_PA.txt', actor_args={}, evolve_args={ 'generations': 1000, 'simulation_reps': 25, 'max_steps': 10000, 'p_mutation': 0.03, 'render_gens': None, 'savenum': 3, 'allow_parallel': False }, render_args={ 'fps': 3, 'max_steps': 5000 })
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 GeneticPerceptronActor as GPA if __name__ == '__main__': env = gym.make('FrozenLake-v0') test_actor_class(GPA, env, savefile='FrozenLake_PA.txt', actor_args={}, evolve_args={ 'generations': 1000, 'simulation_reps': 25, 'max_steps': 10000, 'p_mutation': 0.03, 'render_gens': None, 'savenum': 3, }, render_args={ 'fps': 2, 'max_steps': 5000 })
import gym from testing import test_actor_class from genetics import top_selection from actor import GeneticPerceptronActor as GPA if __name__ == '__main__': env = gym.make('Copy-v0') test_actor_class(GPA, env, savefile='Copy_PA.txt', actor_args={}, evolve_args={ 'generations': 1000, 'simulation_reps': 25, 'max_steps': 10000, 'p_mutation': 0.03, 'selection': lambda p: top_selection(p, cutoff=0.40), 'render_gens': None, 'savenum': 3, 'allow_parallel': False }, render_args={ 'fps': 3, 'max_steps': 5000 })
import gym from testing import test_actor_class from actor import GeneticPerceptronActor as GPA from actor import GeneticNNActor as GNNA if __name__ == '__main__': env = gym.make('Breakout-ram-v0') test_actor_class(GNNA, env, savefile='Breakout_NN_10_10_4_pop_50_pm_50.txt', population_size=50, actor_args={'hidden_layers': [10, 10, 4]}, evolve_args={ 'generations': 1000, 'simulation_reps': 2, 'max_steps': 10000, 'p_mutation': 0.50, 'mutation_scale': 0.10, 'render_gens': None, 'savenum': 1, 'allow_parallel': True }, render_args={ 'fps': 20, 'max_steps': 5000 })
import gym from testing import test_actor_class from actor import GeneticPerceptronActor as GPA if __name__ == '__main__': env = gym.make('MountainCar-v0') test_actor_class(GPA, env, savefile='MountainCar_PA_pop_100_pm_10.txt', population_size=100, actor_args={ }, evolve_args={ 'generations': 1000, 'simulation_reps':5, 'max_steps':10000, 'p_mutation': 0.10, 'render_gens': None, 'savenum': 1, }, render_args={ 'fps': 30, 'max_steps':5000 } )
import gym from testing import test_actor_class from actor import GeneticNNActor as GNNA if __name__ == '__main__': env = gym.make('Pong-ram-v0') test_actor_class(GNNA, env, savefile='Pong_NN_4.txt', actor_args={'hidden_layers': [4]}, evolve_args={ 'generations': 1000, 'simulation_reps': 5, 'max_steps': 10000, 'p_mutation': 0.05, 'render_gens': None, 'savenum': 3, }, render_args={ 'fps': 20, 'max_steps': 5000 })