Python Deep Learning

Model-based approaches

The approaches we've so far shown can do a good job of learning all kinds of tasks, but an agent trained in these ways can still suffer from significant limitations:

• It trains very slowly; a human can learn a game like Pong from a couple of plays, while for Q-learning, it may take millions of playthroughs to get to a similar level.
• For games that require long-term planning, all the techniques perform very badly. Imagine a platform game where a player must retrieve a key from one side of a room to open a door on the other side. There will rarely be a passage of play where this occurs, and even then, the chance of learning that it was the key that lead to the extra reward from the door is miniscule.
• It cannot formulate a strategy ...