Deep deterministic policy gradient

In Chapter 8, Atari Games with Deep Q Network, we looked at how DQN works and we applied DQNs to play Atari games. However, those are discrete environments where we have a finite set of actions. Think of a continuous environment space like training a robot to walk; in those environments it is not feasible to apply Q learning because finding a greedy policy will require a lot of optimization at each and every step. Even if we make this continuous environment discrete, we might lose important features and end up with a huge set of action spaces. It is difficult to attain convergence when we have a huge action space.

So we use a new architecture called Actor Critic with two networks—Actor and Critic. The Actor ...

Get Hands-On Reinforcement Learning with Python now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.