Abstract
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.
Keywords
- Machine Learning: Deep Reinforcement Learning
- Heuristic Search and Game Playing: Game Playing and Machine Learning
- Machine Learning: Probabilistic Machine Learning
