mlsanityData to 14 Oct 2026

The Learning Approach to Games

arXiv 2503.00227 ↗First posted 28 Feb 2025cs.GT econ.TH math.OCcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

This work introduces a unified framework for analyzing games in greater depth. In the existing literature, players' strategies are typically assigned scalar values, and equilibrium concepts are used to identify compatible choices. However, this approach neglects the internal structure of players, thereby failing to accurately model observed behaviors. To address this limitation, we propose an abstract definition of a player, consistent with constructions in reinforcement learning. Instead of defining games as external settings, our framework defines them in terms of the players themselves. This offers a language that enables a deeper connection between games and learning. To illustrate the need for this generality, we study a simple two-player game and show that even in basic settings, a sophisticated player may adopt dynamic strategies that cannot be captured by simpler models or compatibility analysis. For a general definition of a player, we discuss natural conditions on its components and define competition through their behavior. In the discrete setting, we consider players whose estimates largely follow the standard framework from the literature. We explore connections to correlated equilibrium and highlight that dynamic programming naturally applies to all estimates. In the mean-field setting, we exploit symmetry to construct explicit examples of equilibria. Finally, we conclude by examining relations to reinforcement learning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 35 of its 69 references that are in the map. First posted on arXiv 28 Feb 2025.

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35 of 69 references.

References · 35

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
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  10. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  11. 2018Learning Latent Dynamics for Planning from Pixelscited by 1,264
  12. 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,194
23 more
  1. 2018Exploration by Random Network Distillationcited by 993
  2. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 774
  3. 2017A Distributional Perspective on Reinforcement Learningcited by 765
  4. 2020CURL: Contrastive Unsupervised Representations for Reinforcement Learningcited by 760
  5. 2016Deep Exploration via Bootstrapped DQNcited by 527
  6. 2017Distributional Reinforcement Learning with Quantile Regressioncited by 474
  7. 2018Data-Efficient Hierarchical Reinforcement Learningcited by 455
  8. 2016The Option-Critic Architecturecited by 448
  9. 2018Implicit Quantile Networks for Distributional Reinforcement Learningcited by 369
  10. 2020Data-Efficient Reinforcement Learning with Self-Predictive Representationscited by 318
  11. 2020Agent57: Outperforming the Atari Human Benchmarkcited by 303
  12. 2017Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuningcited by 297
  13. 2020Decoupling Representation Learning from Reinforcement Learningcited by 269
  14. 2020Never Give Up: Learning Directed Exploration Strategiescited by 212
  15. 2019Fully Parameterized Quantile Function for Distributional Reinforcement Learningcited by 88
  16. 2020Munchausen Reinforcement Learningcited by 69
  17. 2015Learning in Mean Field Games: the Fictitious Playcited by 38
  18. 2023Unlocking the Power of Representations in Long-term Novelty-based Explorationcited by 9
  19. 2020Correlated equilibria and mean field games: a simple modelcited by 7
  20. 2022Learning Correlated Equilibria in Mean-Field Gamescited by 6
  21. 2022Correlated equilibria for mean field games with progressive strategiescited by 2
  22. 2021Set Values for Mean Field Gamescited by 1
  23. 2023Coarse correlated equilibria for continuous time mean field games in open loop strategiescited by 1

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