Improving Reinforcement Learning Exploration by Autoencoders

Reinforcement learning is a field with massive potential related to solving engineering problems without field knowledge. However, the problem of exploration and exploitation emerges when one tries to balance a system between the learning phase and proper execution. In this paper, a new method is proposed that utilizes autoencoders to manage the exploration rate in an epsilon-greedy exploration algorithm. The error between the real state and the reconstructed state by the autoencoder becomes the base of the exploration-exploitation rate. The proposed method is then examined in two experiments: one benchmark is the cartpole experiment while the other is a gridworld example created for this paper to examine long-term exploration. Both experiments show results such that the proposed method performs better in these scenarios.