Multicopy Reinforcement Learning Agents
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Abstract (arXiv)
This paper examines a novel type of multi-agent problem, in which an agent makes multiple identical copies of itself in order to achieve a single agent task better or more efficiently. This strategy improves performance if the environment is noisy and the task is sometimes unachievable by a single agent copy. We propose a learning algorithm for this multicopy problem which takes advantage of the structure of the value function to efficiently learn how to balance the advantages and costs of adding additional copies.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 27 references that are in the map. First posted on arXiv 19 Sep 2023.
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7 of 27 references.
References · 7
- 2017Reinforcement Learning with Deep Energy-Based Policiescited by 669
- 2017A Brief Survey of Deep Reinforcement Learningcited by 483
- 2019A Review of Cooperative Multi-Agent Deep Reinforcement Learningcited by 147
- 2016Multi-Objective Deep Reinforcement Learningcited by 83
- 2021Deep Multiagent Reinforcement Learning: Challenges and Directionscited by 42
- 2018Multi-Agent Common Knowledge Reinforcement Learningcited by 31
- 2022K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agentscited by 2
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