A heuristic Q-learning architecture for fully exploring a world and deriving an optimal policy by model-based planning

For solving Markov decision processes with incomplete information on robot learning tasks, model-based algorithm makes effective use of gathered data, but usually requires extreme computation. Dyna-Q is an architecture that uses experiences to build a model and uses the model to adjust the policy simultaneously, however, it does not help an agent to explore an environment actively. In, this paper, we present an Exa-Q architecture which learns models and makes plans using learned models to help the reinforcement learning agent explore an environment actively and improve the reinforcement function estimate. As a result, the Exa-Q architecture can identify an environment fully and speed up the learning rate for deriving the optimal policy. Experimental results demonstrate that the proposed method is efficient