The challenge of hidden gifts in multi-agent reinforcement learning

[2505.20579] The challenge of hidden gifts in multi-agent reinforcement learning Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2505.20579 (cs) [Submitted on 26 May 2025 ( v1 ), last revised 18 Jul 2026 (this version, v7)] Title: The challenge of hidden gifts in multi-agent reinforcement learning Authors: Dane Malenfant , Blake A. Richards View a PDF of the paper titled The challenge of hidden gifts in multi-agent reinforcement learning, by Dane Malenfant and 1 other authors View PDF HTML (experimental) Abstract: Sometimes we benefit from actions that others have taken even when we are unaware that they took those actions. For example, if your neighbor chooses not to take a parking spot in front of your house when you are not there, you can benefit, even without being aware that they took this action. These ``hidden gifts'' represent an interesting challenge for multi-agent reinforcement learning (MARL), since assigning credit when the beneficial actions of others are hidden is non-trivial. Here, we study the impact of hidden gifts with a simple MARL task. In this task, agents in a grid-world environment have individual doors to unlock in order to obtain individual rewards. As well, if all the agents unlock their door the group receives a larger collective reward. However, there is only one key for all of the doors, such that the collective reward can only be obtained when the agents drop the key for others after they use it. Notably, there is nothing to indicate to an agent that the other agents have dropped the key, thus this act for others is a ``hidden gift''. We show that several different state-of-the-art MARL algorithms, including MARL specific architectures, fail to learn how to obtain the collective reward in this simple task. Interestingly, we find that decentralized actor-critic policy gradient agents can succeed when we provide them with information about their own action history, but MARL agents still cannot solve the task with action history. Finally, we derive a correction term for policy gradient agents, inspired by learning aware approaches, which reduces the variance in learning and helps them to converge to collective success more reliably. These results show that credit assignment in multi-agent settings can be particularly challenging in the presence of ``hidden gifts'', and demonstrate that self learning-awareness in decentralized agents can benefit these settings. Comments: Published at the third Reinforcement Learning Conference 2026 Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) Cite as: arXiv:2505.20579 [cs.LG] (or arXiv:2505.20579v7 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2505.20579 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Dane Malenfant [ view email ] [v1] Mon, 26 May 2025 23:28:52 UTC (36,368 KB) [v2] Thu, 29 May 2025 13:37:25 UTC (36,368 KB) [v3] Wed, 30 Jul 2025 01:18:05 UTC (36,376 KB) [v4] Mon, 1 Sep 2025 16:56:59 UTC (47,080 KB) [v5] Tue, 30 Sep 2025 20:59:05 UTC (48,353 KB) [v6] Wed, 8 Apr 2026 20:58:11 UTC (22,205 KB) [v7] Sat, 18 Jul 2026 20:07:20 UTC (44,275 KB) Full-text links: Access Paper: View a PDF of the paper titled The challenge of hidden gifts in multi-agent reinforcement learning, by Dane Malenfant and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2025-05 Change to browse by: cs cs.AI cs.MA References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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