Decision Mamba: Reinforcement Learning via Sequence Modeling with Selective State Spaces

arXiv:2403.19925v1 Announce Type: new
Abstract: Decision Transformer, a promising approach that applies Transformer architectures to reinforcement learning, relies on causal self-attention to model sequences of states, actions, and rewards. While this method has shown competitive results, this paper investigates the integration of the Mamba framework, known for its advanced capabilities in efficient and effective sequence modeling, into the Decision Transformer architecture, focusing on the potential performance enhancements in sequential decision-making tasks. Our study systematically evaluates this integration by conducting a …

abstract advanced architectures arxiv attention causal cs.ai cs.lg decision framework integration mamba modeling paper reinforcement reinforcement learning results self-attention spaces state transformer type via