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Reinforcement learning has enjoyed multiple successes in recent years.However, these successes typically require very large amounts of data before anagent achieves acceptable performance. This paper introduces a novel way ofcombating such requirements by leveraging existing (human or agent) knowledge.In particular, this paper uses demonstrations from agents and humans, allowingan untrained agent to quickly achieve high performance. We empirically comparewith, and highlight the weakness of, HAT and CHAT, methods of transferringknowledge from a source agent/human to a target agent. This paper introduces aneffective transfer approach, DRoP, combining the offline knowledge(demonstrations recorded before learning) with online confidence-basedperformance analysis. DRoP dynamically involves the demonstrator's knowledge,integrating it into the reinforcement learning agent's online learning loop toachieve efficient and robust learning.

Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human/Agent's Demonstration