OpenAI Gym CartPole - Deep Q-Learning (dqn framework)

dqn.py

class DQN(object):
    def __init__(self, n_states, n_actions, n_hidden, batch_size, lr, epsilon, gamma, target_replace_iter, memory_capacity):
        self.eval_net, self.target_net = Net(n_states, n_actions, n_hidden), Net(n_states, n_actions, n_hidden)

        self.memory = np.zeros((memory_capacity, n_states * 2 + 2)) # 每個 memory 中的 experience 大小為 (state + next state + reward + action)
        self.optimizer = torch.optim.Adam(self.eval_net.parameters(), lr=lr)
        self.loss_func = nn.MSELoss()
        self.memory_counter = 0
        self.learn_step_counter = 0 # 讓 target network 知道什麼時候要更新

        self.n_states = n_states
        self.n_actions = n_actions
        self.n_hidden = n_hidden
        self.batch_size = batch_size
        self.lr = lr
        self.epsilon = epsilon
        self.gamma = gamma
        self.target_replace_iter = target_replace_iter
        self.memory_capacity = memory_capacity

    def choose_action(self):
    	pass

    def store_transition(self):
    	pass

    def learn(self):
    	pass