REINFORCE issues
In the previous section, we discussed the REINFORCE method, which is a natural extension of the cross-entropy method. Unfortunately, both REINFORCE and the cross-entropy method still suffer from several problems, which make both of them limited to simple environments.
Full episodes are required
First of all, we still need to wait for the full episode to complete before we can start training. Even worse, both REINFORCE and the cross-entropy method behave better with more episodes used for training (just because more episodes means more training data, which means more accurate policy gradients). This situation is fine for short episodes in the CartPole, when in the beginning, we can barely handle the bar for more than 10 steps; but in Pong, it is completely different: every episode can last for hundreds or even thousands of frames. It’s equally bad from the training perspective, as our training batch becomes very large, and from the...
