Reinforcement learning is one of the most reliable methods, which have been used to solve many problems. One of the best reinforcement learning family methods are temporal difference methods. The most important weakness of reinforcement learning methods, such as temporal difference methods, is that these methods have slow convergence rate. Many studies are devoted to solving this problem. One of the proposed solutions to this problem is eligibility traces. Owing to the nature of off-policy methods, combining eligibility traces with off-policy methods requires special attention. In the early learning process for Watkins method (one of the dominant eligibility traces methods), cutting eligibility traces during exploratory actions results in diminishing benefits of eligibility traces method. In this study, we propose a framework to combine eligibility traces with off-policy methods. This research attempts to properly use the information explored during action exploration of the agent; to this end, the decision about applying the eligibility traces during the exploratory actions of the agent is made by means of fuzzy adaptation. We apply this method to find the goal state in the static and dynamic grid world. We compare our approach against the state of the art techniques and show that it outperforms these techniques both in terms of averaged achieved reward and also the convergence time.
Shokri Matin, Seyed Hossein Khasteh, and Amin Aminifar. "Adaptive Fuzzy Watkins: A New Adaptive Approach for Eligibility Traces in Reinforcement Learning." International Journal of Fuzzy Systems 21.5 (2019): 1443-1454.
https://link.springer.com/article/10.1007/s40815-019-00633-x