Reinforcement learning (RL) has had many successes when learning autonomously. This paper and accompanying talk consider how to make use of a non-technical human participant, when available. In particular, we consider the case where a human could 1) provide demonstrations of good behavior, 2) provide online evaluative feedback, or 3) define a curriculum of tasks for the agent to learn on. In all cases, our work has shown such information can be effectively leveraged. After giving a high-level overview of this work, we will highlight a set of open questions and suggest where future work could be usefully focused. 


Title = {Improving Reinforcement Learning with Human Input},
Author = {Matthew E. Taylor},
booktitle = {Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI)},
Year = {2018}

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