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
})
