Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning

• URL: http://arxiv.org/abs/2402.02665v1
• Date: Mon, 5 Feb 2024 01:42:28 GMT
• Title: Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning
• Authors: Peter Vamplew, Cameron Foale, Conor F. Hayes, Patrick Mannion, Enda Howley, Richard Dazeley, Scott Johnson, Johan K\"allstr\"om, Gabriel Ramos, Roxana R\u{a}dulescu, Willem R\"opke, Diederik M. Roijers
• Abstract summary: We extend the utility-based paradigm to the context of single-objective reinforcement learning (RL) We outline multiple potential benefits including the ability to perform multi-policy learning across tasks relating to uncertain objectives, risk-aware RL, discounting, and safe RL. We also examine the algorithmic implications of adopting a utility-based approach.
• Score: 3.292607871053364
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Research in multi-objective reinforcement learning (MORL) has introduced the utility-based paradigm, which makes use of both environmental rewards and a function that defines the utility derived by the user from those rewards. In this paper we extend this paradigm to the context of single-objective reinforcement learning (RL), and outline multiple potential benefits including the ability to perform multi-policy learning across tasks relating to uncertain objectives, risk-aware RL, discounting, and safe RL. We also examine the algorithmic implications of adopting a utility-based approach.

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