REINFORCE एल्गोरिदम को ट्रेन करना

for episode in range(50):
    state, info = env.reset()
    done = False
    episode_reward = 0
    step = 0
    episode_log_probs = torch.tensor([])
    R = 0
    while not done:
        step += 1
        action, log_prob = select_action(policy_network, state)                
        next_state, reward, terminated, truncated, _ = env.step(action)
        done = terminated or truncated
        episode_reward += reward
        # Append to the episode action log probabilities
        episode_log_probs = torch.cat((____, ____))
        # Increment the episode return
        R += (____ ** step) * ____
        state = next_state
    # Calculate the episode loss
    loss = ____ * ____.sum()
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    describe_episode(episode, reward, episode_reward, step)