Hands-On Reinforcement Learning with Python

DRQN

So, why do we need DRQN when our DQN performed at a human level at Atari games? To answer this question, let us understand the problem of the partially observable Markov Decision Process (POMDP). An environment is called a partially observable MDP when we have a limited set of information available about the environment. So far, in the previous chapters, we have seen a fully observable MDP where we know all possible actions and states—although the agent might be unaware of transition and reward probabilities, it had complete knowledge of the environment, for example, a frozen lake environment, where we clearly know about all the states and actions of the environment; we easily modeled that environment as a fully observable MDP. But most ...