Research on Fast Reinforcement Learning

Based on eligibility trace theory, a delayed fast reinforcement learning algorithm DFSARSA(λ) is proposed in this paper. By redefining the eligibility trace and tracking the (TD(λ)) error, the Q-value of reinforcement learning updates may be postponed when they are needed instead of update in each step as traditional SARSA(λ). The update computing complexity is reduced from O(|S||A|) to O(|A|) compared with SARSA(λ) and the speed of the reinforcement learning is improved greatly. Simulation results show the method's validity.