An Experience Replay Method Based on Tree Structure for Reinforcement Learning

Q-Learning, which is a well-known model-free reinforcement learning algorithm, a learning agent explores an environment to update a state-action function. In reinforcement learning, the agent does not require information about the environment in advance, so an interaction between the agent and the environment is for collecting the real experiences that is also an expensive and time-consuming process. Therefore, to reduce the burden of the interaction, sample efficiency becomes an important role in reinforcement learning. This study proposes an adaptive tree structure integrating with experience replay for Q-Learning, called ERTS-Q. In ERTS-Q method, Q-Learning is used for policy learning, a tree structure establishes a virtual model which perceives two different continuous states after each state transaction, and then the variations of the continuous state are calculated. After each state transition, all states with highly similar variation are aggregated into the same leaf nodes. Otherwise, new leaf nodes will be produced. For experience replay, the tree structure predicts the next state and reward based on the statistical information that is stored in tree nodes. The virtual experiences produced by the tree structure are used for achieving extra learning. Simulations of the mountain car and a maze environment are performed to verify the validity of the proposed modeling learning approach.