A Reinforcement Learning Method Based

Reinforcement learning is a hard problem and the majority of the existing algorithms su#er from poor convergence properties for di#cult problems. In this paper we propose a new reinforcement learning method, that utilizes the power of global optimization methods such as simulated annealing. Specifically, we use a particularly powerful version of simulated annealing called Adaptive Simulated Annealing (ASA) [3]. Towards this end we consider a batch formulation for the reinforcement learning problem, unlike the online formulation almost always used. The advantage of the batch formulation is that it allows state-of-the-art optimization procedures to be employed, and thus can lead to 1 further improvements in algorithmic convergence properties. The proposed algorithm is applied to a decision making test problem, and it is shown to obtain better results than the conventional Q-learning algorithm