Reinforcement Learning with Non-Markovian Rewards

6 Sep 2019 Mridul Agarwal Vaneet Aggarwal

Reinforcement Learning (RL) algorithms such as DQN owe their success to Markov Decision Processes, and the fact that maximizing the sum of rewards allows using backward induction and reduce to the Bellman optimality equation. However, many real-world problems require optimization of an objective that is non-linear in cumulative rewards for which dynamic programming cannot be applied directly... (read more)

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