Deep Reinforcement Learning with POMDPs

Abstract

Recent work has shown that Deep Q-Networks (DQNs) are capable of learning human-level control policies on a variety of different Atari 2600 games [1]. Other work has looked at treating the Atari problem as a partially observable Markov decision process (POMDP) by adding imperfect state information through image flickering [2]. However, these approaches… (More)

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