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The expanding role of reinforcement learning (RL) in safety-critical system design has promoted ω-automata as a way to express learning requirements—often non-Markovian—with greater ease of expression and interpretation than scalar reward signals. However, real-world sequential decision making situations often involve multiple, potentially conflicting, objectives. Two dominant approaches to express relative preferences over multiple objectives are: (1)weighted preference, where the decision maker provides scalar weights for various objectives, and (2)lexicographic preference, where the decision maker provides an order over the objectives such that any amount of satisfaction of a higher-ordered objective is preferable to any amount of a lower-ordered one. In this article, we study and develop RL algorithms to compute optimal strategies in Markov decision processes against multiple ω-regular objectives under weighted and lexicographic preferences. We provide a translation from multiple ω-regular objectives to a scalar reward signal that is bothfaithful(maximising reward means maximising probability of achieving the objectives under the corresponding preference) andeffective(RL quickly converges to optimal strategies). We have implemented the translations in a formal reinforcement learning tool,Mungojerrie, and we present an experimental evaluation of our technique on benchmark learning problems.
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Group Fairness in Reinforcement Learning via Multi-Objective Rewards
Recent works extend classification group fairness measures to sequential decision processes such as reinforcement learning (RL) by measuring fairness as the difference in decisionmaker utility (e.g. accuracy) of each group. This approach suffers when decision-maker utility is not perfectly aligned with group utility, such as in repeat loan applications where a false positive (loan default) impacts the groups (applicants) and decision-maker (lender) by different magnitudes. Some works remedy this by measuring fairness in terms of group utility, typically referred to as their "qualification", but few works offer solutions that yield group qualification equality. Those that do are prone to violating the "no-harm" principle where one or more groups’ qualifications are lowered in order to achieve equality. In this work, we characterize this problem space as having three implicit objectives: maximizing decision-maker utility, maximizing group qualification, and minimizing the difference in qualification between groups. We provide a RL policy learning technique that optimizes for these objectives directly by constructing a multi-objective reward function that encodes these objectives as distinct reward signals. Under suitable parameterizations our approach is guaranteed to respect the "no-harm" principle.
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- Award ID(s):
- 1939743
- PAR ID:
- 10586337
- Publisher / Repository:
- Transactions on Machine Learning Research
- Date Published:
- Journal Name:
- Transactions on machine learning research
- ISSN:
- 2835-8856
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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