Nonparametric adaptive control of discounted stochastic systems with compact state space

1990 ◽  
Vol 65 (2) ◽  
pp. 191-207 ◽  
Author(s):  
R. Cavazos-Cadena
Keyword(s):  
Adaptive Control ◽  
State Space ◽  
Stochastic Systems ◽  
Compact State

scholarly journals Generalized Representation Learning Methods for Deep Reinforcement Learning

2020 ◽  
Author(s):  
Hanhua Zhu
Keyword(s):  
Reinforcement Learning ◽  
State Space ◽  
Representation Learning ◽  
Learning Methods ◽  
New Methods ◽  
Compact State

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.

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