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A Survey on Transformers in Reinforcement Learning

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arxiv 2301.03044 v3 pith:6G3KNXKL submitted 2023-01-08 cs.LG cs.AI

Wenzhe Li , Hao Luo , Zichuan Lin , Chongjie Zhang , Zongqing Lu , Deheng Ye This is my paper
classification cs.LGcs.AI
keywords transformersbeenlearningreinforcementappearedarchitecturebroughtchallenges
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Transformer has been considered the dominating neural architecture in NLP and CV, mostly under supervised settings. Recently, a similar surge of using Transformers has appeared in the domain of reinforcement learning (RL), but it is faced with unique design choices and challenges brought by the nature of RL. However, the evolution of Transformers in RL has not yet been well unraveled. In this paper, we seek to systematically review motivations and progress on using Transformers in RL, provide a taxonomy on existing works, discuss each sub-field, and summarize future prospects.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Odysseus: Scaling VLMs to 100+ Turn Decision-Making in Games via Reinforcement Learning

    cs.LG 2026-05 unverdicted novelty 6.0 of 10

    Odysseus adapts PPO with a turn-level critic and leverages pretrained VLM action priors to train agents achieving at least 3x average game progress over frontier models in long-horizon Super Mario Land.

  2. Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures

    quant-ph 2024-06 unverdicted novelty 6.0 of 10

    An attention-based DRL agent with Transformer encoder and GNN learns heuristics for qubit-to-core allocation in multi-core quantum systems to minimize state transfers and online compilation time.

  3. Reinforcement Learning in hyperbolic space for multi-step reasoning

    cs.LG 2025-07 reject novelty 4.0 of 10

    Hyperbolic transformer policies are claimed to beat vanilla transformer policies by 32-45% on a handful of reasoning and control problems, but the evidence is too weak to support the claim.

  4. Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks

    eess.SP 2026-05 unverdicted novelty 1.0 of 10

    A survey of Transformer-enhanced reinforcement learning fundamentals and applications in communication networks covering resource allocation, computation offloading, routing, trajectory control, and security.

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