def main():
"""
run a trained model for the pong problem
"""
env = gym.make("PongNoFrameskip-v4")
env = deepq.wrap_atari_dqn(env)
model = DeepQ.load("pong_model.pkl", env)
while True:
obs, done = env.reset(), False
episode_rew = 0
while not done:
env.render()
action, _ = model.predict(obs)
obs, rew, done, _ = env.step(action)
episode_rew += rew
print("Episode reward", episode_rew)
def main(args):
"""
run a trained model for the mountain car problem
:param args: (ArgumentParser) the input arguments
"""
env = gym.make("MountainCar-v0")
model = DeepQ.load("mountaincar_model.pkl", env)
while True:
obs, done = env.reset(), False
episode_rew = 0
while not done:
if not args.no_render:
env.render()
action, _ = model.predict(obs)
obs, rew, done, _ = env.step(action)
episode_rew += rew
print("Episode reward", episode_rew)
# No render is only used for automatic testing
if args.no_render:
break