Training the reinforcement learning agent at the Gym

The procedure to train the Q-learning agent may look familiar to you already, because it has many of the same lines of code as, and also a similar structure to, the boilerplate code that we used before. Instead of choosing a random action from the environment's actions space, we now get the action from the agent using the agent.get_action(obs) method. We also call the agent.learn(obs, action, reward, next_obs) method after sending the agent's action to the environment and receiving the feedback. The training function is listed here:

def train(agent, env):
    best_reward = -float('inf')
    for episode in range(MAX_NUM_EPISODES):
        done = False
        obs = env.reset()
        total_reward = 0.0
        while not done:
            action = agent.get_action(obs)
            next_obs, reward, done, info = env.step(action)
            agent.learn(obs, action, reward, next_obs)
            obs = next_obs
            total_reward += reward...