def __init__(self):
env = gym.envs.make("PongDeterministic-v4")
self.Q_target = DQN.Mynet(env.observation_space, env.action_space).to(device)
self.Q_policy = DQN.Mynet(env.observation_space, env.action_space).to(device)
self.Q_target.load_state_dict(self.Q_policy.state_dict())
self.Q_target.eval()
self.env = env
self.pool = DQN.ReplyMemory(15000)
self.gramma = GRAMMA
self.alpha = ALPHA
self.epsilon = EPSILON
self.ImageProcess = DQN.ImageProcess()
return torch.cat(obb).to(device).unsqueeze(0)
if __name__ == '__main__':
env = gym.envs.make("PongDeterministic-v4")
V = DQN.Mynet(env.observation_space, env.action_space)
# V = DQN.Mynet()
# with open('./save_model/499.pt', 'r') as f:
V.load_state_dict(torch.load(r'C:\Users\lingse\Desktop\新建文件夹\RL_home_work-master\Q_save_model\899_pong_new.pt'))
V.eval()
observation = [None for i in range(5)]
import numpy as np
# c = V(state).max(1)[1].view(1, 1)
I = DQN.ImageProcess()
while True:
state = env.reset()
state = I.ColorMat2Binary(state)
state_shadow = np.stack((state, state, state, state), axis=2)
state_now = transfor_o(state_shadow)
import time
while True:
env.render()
time.sleep(0.01)
action = V(state_now).max(1)[1].view(1, 1)
if action[0][0] == 0:
do_action = [[2]]
else:
do_action = [[5]]
observation1, reward, done, _ = env.step(do_action)
