Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction Report
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Abstract (arXiv)
In this report, we present results reproductions for several core algorithms implemented in the OpenSpiel framework for learning in games. The primary contribution of this work is a validation of OpenSpiel's re-implemented search and Reinforcement Learning algorithms against the results reported in their respective originating works. Additionally, we provide complete documentation of hyperparameters and source code required to reproduce these experiments easily and exactly.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 21 references that are in the map. First posted on arXiv 27 Feb 2021.
Builds on
13 of 21 references.
References · 13
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
- 2017A Unified Game-Theoretic Approach to Multiagent Reinforcement Learningcited by 268
- 2019OpenSpiel: A Framework for Reinforcement Learning in Gamescited by 186
- 2018A Survey and Critique of Multiagent Deep Reinforcement Learningcited by 177
- 2016Deep Reinforcement Learning from Self-Play in Imperfect-Information Gamescited by 158
- 2018Deep Counterfactual Regret Minimizationcited by 103
- 2020Game-Theoretic Multiagent Reinforcement Learningcited by 94
- 2019A Generalized Training Approach for Multiagent Learningcited by 54
- 2018Actor-Critic Policy Optimization in Partially Observable Multiagent Environmentscited by 35
- 2019Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descentcited by 31
- 2019Single Deep Counterfactual Regret Minimizationcited by 20
- 2020The Advantage Regret-Matching Actor-Criticcited by 17
1 more
- 2014Solving Games with Functional Regret Estimationcited by 12
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.