ddqn.py

    env = gym.make('CartPole-v1')
    state_size = env.observation_space.shape[0]
    action_size = env.action_space.n
    agent = DQNAgent(state_size, action_size)
    # agent.load("./save/cartpole-ddqn.h5")
    done = False
    batch_size = 32

    for e in range(EPISODES):
        state = env.reset()
        state = np.reshape(state, [1, state_size])
        for time in range(500):
            # env.render()
            action = agent.act(state)
            next_state, reward, done, _ = env.step(action)
            #reward = reward if not done else -10
            x,x_dot,theta,theta_dot = next_state
            r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8
            r2 = (env.theta_threshold_radians - abs(theta)) / env.theta_threshold_radians - 0.5
            reward = r1 + r2
            
            next_state = np.reshape(next_state, [1, state_size])
            agent.memorize(state, action, reward, next_state, done)
            state = next_state
            if done:
                agent.update_target_model()
                print("episode: {}/{}, score: {}, e: {:.2}"
                      .format(e, EPISODES, time, agent.epsilon))
                break
            if len(agent.memory) > batch_size:
                agent.replay(batch_size)