How to train your agent: active learning from human preferences and justifications in safety-critical environments

Kazantzidis, Ilias, Norman, Timothy, Du, Yali and Freeman, Christopher (2022) How to train your agent: active learning from human preferences and justifications in safety-critical environments. International Conference on Autonomous Agents and Milti-Agent Systems 2022, Auckland, Auckland, New Zealand. 09 - 13 May 2022.

Record type: Conference or Workshop Item (Paper)

Abstract

Training reinforcement learning agents in real-world environments is costly, particularly for safety-critical applications. Human input can enable an agent to learn a good policy while avoiding unsafe actions, but at the cost of bothering the human with repeated queries. We present a model for safe learning in safety-critical environments from human input that minimises bother cost. Our model, JPAL-HA, proposes an efficient mechanism to harness human preferences and justifications to significantly improve safety during the learning process without increasing the number of interactions with a user. We show this with both simulation and human experiments.

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