Generalized Representation Learning Methods for Deep Reinforcement Learning
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Doctoral Consortium. Pages 5216-5217. https://doi.org/10.24963/ijcai.2020/748
Deep reinforcement learning (DRL) increases the successful applications of reinforcement learning (RL) techniques but also brings challenges such as low sample efficiency. In this work, I propose generalized representation learning methods to obtain compact state space suitable for RL from a raw observation state. I expect my new methods will increase sample efficiency of RL by understandable representations of state and therefore improve the performance of RL.
Machine Learning: Deep Reinforcement Learning
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