mlsanityData to 14 Oct 2026

Playing Non-Embedded Card-Based Games with Reinforcement Learning

arXiv 2504.04783 ↗First posted 7 Apr 2025cs.LGcited by 0

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

Abstract (arXiv)

Significant progress has been made in AI for games, including board games, MOBA, and RTS games. However, complex agents are typically developed in an embedded manner, directly accessing game state information, unlike human players who rely on noisy visual data, leading to unfair competition. Developing complex non-embedded agents remains challenging, especially in card-based RTS games with complex features and large state spaces. We propose a non-embedded offline reinforcement learning training strategy using visual inputs to achieve real-time autonomous gameplay in the RTS game Clash Royale. Due to the lack of a object detection dataset for this game, we designed an efficient generative object detection dataset for training. We extract features using state-of-the-art object detection and optical character recognition models. Our method enables real-time image acquisition, perception feature fusion, decision-making, and control on mobile devices, successfully defeating built-in AI opponents. All code is open-sourced at https://github.com/wty-yy/katacr.

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 21 references that are in the map. First posted on arXiv 7 Apr 2025.

Builds on

9 of 21 references.

References · 9

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2017Proximal Policy Optimization Algorithmscited by 15,215
  4. 2023Segment Anythingcited by 8,234
  5. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,809
  6. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,546
  7. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
  8. 2021ByteTrack: Multi-Object Tracking by Associating Every Detection Boxcited by 823
  9. 2021StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learningcited by 27

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  2. 2024Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning Benchmarks
  3. 2024CNN-based Game State Detection for a Foosball Table
  4. 2024Dominion: A New Frontier for AI Research
  5. 2025AI Agents for the Dhumbal Card Game: A Comparative Study
  6. 2019Hierarchical Reinforcement Learning for Multi-agent MOBA Game
  7. 2025Human-Level Competitive Pok'emon via Scalable Offline Reinforcement Learning with Transformers
  8. 2026"Pharos Night: Crown Pursuit": An AI-Native Deck-Building and Tactical Arena Game Design Based on Multi-Agent Systems