Mamba as Decision Maker: Exploring Multi-scale Sequence Modeling in Offline Reinforcement Learning

• URL: http://arxiv.org/abs/2406.02013v2
• Date: Wed, 11 Sep 2024 10:48:49 GMT
• Title: Mamba as Decision Maker: Exploring Multi-scale Sequence Modeling in Offline Reinforcement Learning
• Authors: Jiahang Cao, Qiang Zhang, Ziqing Wang, Jingkai Sun, Jiaxu Wang, Hao Cheng, Yecheng Shao, Wen Zhao, Gang Han, Yijie Guo, Renjing Xu,
• Abstract summary: We propose a novel action predictor sequence, named Mamba Decision Maker (MambaDM) MambaDM is expected to be a promising alternative for sequence modeling paradigms, owing to its efficient modeling of multi-scale dependencies. This paper delves into the sequence modeling capabilities of MambaDM in the RL domain, paving the way for future advancements.
• Score: 16.23977055134524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequential modeling has demonstrated remarkable capabilities in offline reinforcement learning (RL), with Decision Transformer (DT) being one of the most notable representatives, achieving significant success. However, RL trajectories possess unique properties to be distinguished from the conventional sequence (e.g., text or audio): (1) local correlation, where the next states in RL are theoretically determined solely by current states and actions based on the Markov Decision Process (MDP), and (2) global correlation, where each step's features are related to long-term historical information due to the time-continuous nature of trajectories. In this paper, we propose a novel action sequence predictor, named Mamba Decision Maker (MambaDM), where Mamba is expected to be a promising alternative for sequence modeling paradigms, owing to its efficient modeling of multi-scale dependencies. In particular, we introduce a novel mixer module that proficiently extracts and integrates both global and local features of the input sequence, effectively capturing interrelationships in RL datasets. Extensive experiments demonstrate that MambaDM achieves state-of-the-art performance in Atari and OpenAI Gym datasets. Furthermore, we empirically investigate the scaling laws of MambaDM, finding that increasing model size does not bring performance improvement, but scaling the dataset amount by 2x for MambaDM can obtain up to 33.7% score improvement on Atari dataset. This paper delves into the sequence modeling capabilities of MambaDM in the RL domain, paving the way for future advancements in robust and efficient decision-making systems.

Related papers

• Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning [54.19222454702032]
  Continual Learning aims to equip AI models with the ability to learn a sequence of tasks over time, without forgetting previously learned knowledge. State Space Models (SSMs) have achieved notable success in computer vision. We introduce Mamba-CL, a framework that continuously fine-tunes the core SSMs of the large-scale Mamba foundation model.
  arXiv  Detail & Related papers  (2024-11-23T06:36:16Z)
• ReMamba: Equip Mamba with Effective Long-Sequence Modeling [50.530839868893786]
  We propose ReMamba, which enhances Mamba's ability to comprehend long contexts. ReMamba incorporates selective compression and adaptation techniques within a two-stage re-forward process.
  arXiv  Detail & Related papers  (2024-08-28T02:47:27Z)
• Bidirectional Gated Mamba for Sequential Recommendation [56.85338055215429]
  Mamba, a recent advancement, has exhibited exceptional performance in time series prediction. We introduce a new framework named Selective Gated Mamba ( SIGMA) for Sequential Recommendation. Our results indicate that SIGMA outperforms current models on five real-world datasets.
  arXiv  Detail & Related papers  (2024-08-21T09:12:59Z)
• Integrating Multi-Modal Input Token Mixer Into Mamba-Based Decision Models: Decision MetaMamba [0.0]
  Sequence modeling with State Space models (SSMs) has demonstrated performance surpassing that of Transformers in various tasks. However, decision models based on Mamba, a state-of-the-art SSM, failed to achieve superior performance compared to enhanced Decision Transformers. We propose the Decision MetaMamba (DMM), which augments Mamba with a token mixer in its input layer.
  arXiv  Detail & Related papers  (2024-08-20T03:35:28Z)
• Mamba-Spike: Enhancing the Mamba Architecture with a Spiking Front-End for Efficient Temporal Data Processing [4.673285689826945]
  Mamba-Spike is a novel neuromorphic architecture that integrates a spiking front-end with the Mamba backbone to achieve efficient temporal data processing. The architecture consistently outperforms state-of-the-art baselines, achieving higher accuracy, lower latency, and improved energy efficiency.
  arXiv  Detail & Related papers  (2024-08-04T14:10:33Z)
• DiM-Gesture: Co-Speech Gesture Generation with Adaptive Layer Normalization Mamba-2 framework [2.187990941788468]
  generative model crafted to create highly personalized 3D full-body gestures solely from raw speech audio. Model integrates a Mamba-based fuzzy feature extractor with a non-autoregressive Adaptive Layer Normalization (AdaLN) Mamba-2 diffusion architecture.
  arXiv  Detail & Related papers  (2024-08-01T08:22:47Z)
• MambaLRP: Explaining Selective State Space Sequence Models [18.133138020777295]
  Recent sequence modeling approaches using selective state space sequence models, referred to as Mamba models, have seen a surge of interest. These models allow efficient processing of long sequences in linear time and are rapidly being adopted in a wide range of applications such as language modeling. To foster their reliable use in real-world scenarios, it is crucial to augment their transparency.
  arXiv  Detail & Related papers  (2024-06-11T12:15:47Z)
• Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RL [57.202733701029594]
  Decision Mamba is a novel multi-grained state space model with a self-evolving policy learning strategy. To mitigate the overfitting issue on noisy trajectories, a self-evolving policy is proposed by using progressive regularization. The policy evolves by using its own past knowledge to refine the suboptimal actions, thus enhancing its robustness on noisy demonstrations.
  arXiv  Detail & Related papers  (2024-06-08T10:12:00Z)
• CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series Forecasting [18.50360049235537]
  Mamba, a state space model, has emerged with robust sequence and feature mixing capabilities. Capturing cross-channel dependencies is critical in enhancing performance of time series prediction. We introduce a refined Mamba variant tailored for time series forecasting.
  arXiv  Detail & Related papers  (2024-06-08T01:32:44Z)
• Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration [87.53543137162488]
  We propose an easy-to-implement online reinforcement learning (online RL) framework called textttMEX. textttMEX integrates estimation and planning components while balancing exploration exploitation automatically. It can outperform baselines by a stable margin in various MuJoCo environments with sparse rewards.
  arXiv  Detail & Related papers  (2023-05-29T17:25:26Z)
• Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis [96.46952672172021]
  Bi-Bimodal Fusion Network (BBFN) is a novel end-to-end network that performs fusion on pairwise modality representations. Model takes two bimodal pairs as input due to known information imbalance among modalities.
  arXiv  Detail & Related papers  (2021-07-28T23:33:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.