mlsanityData to 14 Oct 2026

Evolutionary Optimization of Deep Learning Agents for Sparrow Mahjong

arXiv 2508.07522 ↗First posted 11 Aug 2025cs.NEcited by 0

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

Abstract (arXiv)

We present Evo-Sparrow, a deep learning-based agent for AI decision-making in Sparrow Mahjong, trained by optimizing Long Short-Term Memory (LSTM) networks using Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Our model evaluates board states and optimizes decision policies in a non-deterministic, partially observable game environment. Empirical analysis conducted over a significant number of simulations demonstrates that our model outperforms both random and rule-based agents, and achieves performance comparable to a Proximal Policy Optimization (PPO) baseline, indicating strong strategic play and robust policy quality. By combining deep learning with evolutionary optimization, our approach provides a computationally effective alternative to traditional reinforcement learning and gradient-based optimization methods. This research contributes to the broader field of AI game playing, demonstrating the viability of hybrid learning strategies for complex stochastic games. These findings also offer potential applications in adaptive decision-making and strategic AI development beyond Sparrow Mahjong.

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 9 of its 24 references that are in the map. First posted on arXiv 11 Aug 2025.

Builds on

9 of 24 references.

References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  4. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
  5. 2019The Hanabi Challenge: A New Frontier for AI Researchcited by 197
  6. 2020Suphx: Mastering Mahjong with Deep Reinforcement Learningcited by 58
  7. 2023Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learningcited by 51
  8. 2018Depth-Limited Solving for Imperfect-Information Gamescited by 27
  9. 2019Building a Computer Mahjong Player via Deep Convolutional Neural Networkscited by 3

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  4. 2026Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition
  5. 2024Mastering the Game of Guandan with Deep Reinforcement Learning and Behavior Regulating
  6. 2025Deep Reinforcement Learning Xiangqi Player with Monte Carlo Tree Search
  7. 2026Global Policy-Space Response Oracles for Two-Player Zero-Sum Games
  8. 2017Thinking Fast and Slow with Deep Learning and Tree Search