Peer-Reviewed Journal Details
Mandatory Fields
Mannion, P; Devlin, S; Mason, K; Duggan, J; Howley, E
2017
November
Neurocomputing
Policy invariance under reward transformations for multi-objective reinforcement learning
Published
WOS: 3 ()
Optional Fields
Reinforcement learning Multi-objective Potential-based Reward shaping Multi-agent systems
263
60
73
Reinforcement Learning (RL) is a powerful and well-studied Machine Learning paradigm, where an agent learns to improve its performance in an environment by maximising a reward signal. In multi-objective Reinforcement Learning (MORL) the reward signal is a vector, where each component represents the performance on a different objective. Reward shaping is a well-established family of techniques that have been successfully used to improve the performance and learning speed of RL agents in single-objective problems. The basic premise of reward shaping is to add an additional shaping reward to the reward naturally received from the environment, to incorporate domain knowledge and guide an agent's exploration. Potential-Based Reward Shaping (PBRS) is a specific form of reward shaping that offers additional guarantees. In this paper, we extend the theoretical guarantees of PBRS to MORL problems. Specifically, we provide theoretical proof that PBRS does not alter the true Pareto front in both single- and multi-agent MORL. We also contribute the first published empirical studies of the effect of PBRS in single- and multi-agent MORL problems. (C) 2017 Elsevier B.V. All rights reserved.
10.1016/j.neucom.2017.05.090
Grant Details
Publication Themes
Informatics, Physical and Computational Sciences