Summary of Mamba As Decision Maker: Exploring Multi-scale Sequence Modeling in Offline Reinforcement Learning, by Jiahang Cao et al.
Mamba as Decision Maker: Exploring Multi-scale Sequence Modeling in Offline Reinforcement Learning
by Jiahang Cao, Qiang Zhang, Ziqing Wang, Jingkai Sun, Jiaxu Wang, Hao Cheng, Yecheng Shao, Wen Zhao, Gang Han, Yijie Guo, Renjing Xu
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Sequential modeling has made significant progress in offline reinforcement learning (RL), with Decision Transformer (DT) being a notable representative. However, RL trajectories possess unique properties that distinguish them from conventional sequences: local correlation, where next states are determined by current states and actions based on Markov Decision Process (MDP), and global correlation, where each step’s features are related to long-term historical information due to time-continuous nature of trajectories. This paper proposes Mamba Decision Maker (MambaDM) as a novel action sequence predictor that efficiently models multi-scale dependencies. The proposed mixer module extracts and integrates both local and global features of the input sequence, effectively capturing interrelationships in RL datasets. Experimental results demonstrate state-of-the-art performance on Atari and OpenAI Gym datasets. This paper explores the sequence modeling capabilities of MambaDM in the RL domain, paving the way for future advancements in robust and efficient decision-making systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mamba Decision Maker (MambaDM) is a new action sequence predictor that can help make decisions better. It’s good at learning from data and making choices based on what it knows. The paper talks about how MambaDM works and shows that it can do really well in games like Atari and OpenAI Gym. It also explains why this matters, and what we might be able to do with this new technology. |
Keywords
» Artificial intelligence » Reinforcement learning » Transformer
