ADAPTER-RL: Adaptation of Any Agent using Reinforcement Learning
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Abstract (arXiv)
Deep Reinforcement Learning (DRL) agents frequently face challenges in adapting to tasks outside their training distribution, including issues with over-fitting, catastrophic forgetting and sample inefficiency. Although the application of adapters has proven effective in supervised learning contexts such as natural language processing and computer vision, their potential within the DRL domain remains largely unexplored. This paper delves into the integration of adapters in reinforcement learning, presenting an innovative adaptation strategy that demonstrates enhanced training efficiency and improvement of the base-agent, experimentally in the nanoRTS environment, a real-time strategy (RTS) game simulation. Our proposed universal approach is not only compatible with pre-trained neural networks but also with rule-based agents, offering a means to integrate human expertise.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 43 references that are in the map. First posted on arXiv 20 Nov 2023.
Builds on
22 of 43 references.
References · 22
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- 2020MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfercited by 443
- 2021VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Taskscited by 319
10 more
- 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 294
- 2018Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learningcited by 270
- 2019Simple, Scalable Adaptation for Neural Machine Translationcited by 239
- 2020AdapterDrop: On the Efficiency of Adapters in Transformerscited by 223
- 2018Assessing Generalization in Deep Reinforcement Learningcited by 132
- 2020Towards Playing Full MOBA Games with Deep Reinforcement Learningcited by 78
- 2019Accelerating Self-Play Learning in Gocited by 60
- 2016Towards Deep Symbolic Reinforcement Learningcited by 42
- 2021Gym-μRTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learningcited by 19
- 2020Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kingscited by 18
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2 papers.