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Reinforcement Learning has emerged as a strong alternative to solveoptimization tasks efficiently. The use of these algorithms highly depends onthe feedback signals provided by the environment in charge of informing abouthow good (or bad) the decisions made by the learned agent are. Unfortunately,in a broad range of problems the design of a good reward function is nottrivial, so in such cases sparse reward signals are instead adopted. The lackof a dense reward function poses new challenges, mostly related to exploration.Imitation Learning has addressed those problems by leveraging demonstrationsfrom experts. In the absence of an expert (and its subsequent demonstrations),an option is to prioritize well-suited exploration experiences collected by theagent in order to bootstrap its learning process with good explorationbehaviors. However, this solution highly depends on the ability of the agent todiscover such trajectories in the early stages of its learning process. Totackle this issue, we propose to combine imitation learning with intrinsicmotivation, two of the most widely adopted techniques to address problems withsparse reward. In this work intrinsic motivation is used to encourage the agentto explore the environment based on its curiosity, whereas imitation learningallows repeating the most promising experiences to accelerate the learningprocess. This combination is shown to yield an improved performance and bettergeneralization in procedurally-generated environments, outperforming previouslyreported self-imitation learning methods and achieving equal or better sampleefficiency with respect to intrinsic motivation in isolation.

Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation