Online reinforcement learning by Bayesian inference

Policy evaluation has long been one of the core issues of the online reinforcement learning, especially in the continuous state domain. In this paper, the issue is addressed by employing Gaussian processes to represent the action value function from the probability perspective. By modeling the return as a stochastic variable, the action value function can sequentially update according to observed variables such as state and reward by Bayesian inference during the policy evaluation. The update rule shows that it is a temporal difference learning method with the learning rate determined by the uncertainty of a collected sample. Incorporating the policy evaluation method with the ∈-greedy action selection method, we propose an online reinforcement learning algorithm referred as to Bayesian-SARSA. It is tested on some benchmark problems and the empirical results verifies its effectiveness.