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

How to train your agent: active learning from human preferences and justifications in safety-critical environments
How to train your agent: active learning from human preferences and justifications in safety-critical environments
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.
paper, human-agent interaction, Reinforcement Learning, Supervised learning, human-robot interface
Kazantzidis, Ilias
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Norman, Timothy
663e522f-807c-4569-9201-dc141c8eb50d
Du, Yali
0b0d4eef-0820-4753-b384-72db5058df32
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Kazantzidis, Ilias
10862613-d212-44fb-980c-ff8c6d0c4a95
Norman, Timothy
663e522f-807c-4569-9201-dc141c8eb50d
Du, Yali
0b0d4eef-0820-4753-b384-72db5058df32
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815

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. (In Press)

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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More information

Accepted/In Press date: 2022
Venue - Dates: International Conference on Autonomous Agents and Milti-Agent Systems 2022, Auckland, Auckland, New Zealand, 2022-05-09 - 2022-05-13
Keywords: paper, human-agent interaction, Reinforcement Learning, Supervised learning, human-robot interface

Identifiers

Local EPrints ID: 454808
URI: http://eprints.soton.ac.uk/id/eprint/454808
PURE UUID: eb507c70-5ab0-427d-9507-632bc06f9eb5
ORCID for Ilias Kazantzidis: ORCID iD orcid.org/0000-0002-1127-3843
ORCID for Timothy Norman: ORCID iD orcid.org/0000-0002-6387-4034

Catalogue record

Date deposited: 24 Feb 2022 21:49
Last modified: 04 Aug 2022 02:04

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Contributors

Author: Ilias Kazantzidis ORCID iD
Author: Timothy Norman ORCID iD
Author: Yali Du
Author: Christopher Freeman

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