ddpg.py 文件源码

def __init__(self,env, is_batch_norm):
        self.env = env 
        self.num_states = env.observation_space.shape[0]
        self.num_actions = env.action_space.shape[0]


        if is_batch_norm:
            self.critic_net = CriticNet_bn(self.num_states, self.num_actions) 
            self.actor_net = ActorNet_bn(self.num_states, self.num_actions)

        else:
            self.critic_net = CriticNet(self.num_states, self.num_actions) 
            self.actor_net = ActorNet(self.num_states, self.num_actions)

        #Initialize Buffer Network:
        self.replay_memory = deque()

        #Intialize time step:
        self.time_step = 0
        self.counter = 0

        action_max = np.array(env.action_space.high).tolist()
        action_min = np.array(env.action_space.low).tolist()        
        action_bounds = [action_max,action_min] 
        self.grad_inv = grad_inverter(action_bounds)