Online Reinforcement Learning with Passive Memory
This paper considers an online reinforcement learning algorithm that leverages pre-collected data (passive memory) from the environment for online interaction. We show that using passive memory improves performance and further provide theoretical guarantees for regret that turns out to be near-minimax optimal. Results show that the quality of passive memory determines sub-optimality of the incurred regret. The proposed approach and results hold in both continuous and discrete state-action spaces.
Code (0)
등록된 구현이 없습니다.
Tasks
reinforcement-learningReinforcement LearningSimilar Papers 제목 키워드 기반
Interactive Memory Learning for Long-Term Conversations
Recent advancements in large language models have significantly enhanced the capabilities of agents in modeling long-term conversations. Despite these successes, existing approaches typically adopt a static heuristic par…
Reinforcement LearningTest-time AdaptationWhat Memory Do GUI Agents Really Need? From Passive Records to Active Task-Driving States
Mobile GUI agents increasingly face long-horizon tasks that require reading, updating, and reusing task-relevant data across pages and applications. Existing methods treat memory largely as passive storage, where past ob…
Reinforcement LearningEventMemAgent: Hierarchical Event-Centric Memory for Online Video Understanding with Adaptive Tool Use
Online video understanding requires models to perform continuous perception and long-range reasoning within potentially infinite visual streams. Its fundamental challenge lies in the conflict between the unbounded nature…
Reinforcement LearningFrom Passive Retrieval to Active Memory Navigation: Learning to Use Memory as a Structured Action Space
Long-term user memory is essential for personalized conversational agents, yet many memory systems still expose memory through passive retrieval interfaces, making the model a consumer of pre-selected evidence. We introd…
Reinforcement LearningMemory Lens: How Much Memory Does an Agent Use?
We propose a new method to study the internal memory used by reinforcement learning policies. We estimate the amount of relevant past information by estimating mutual information between behavior histories and the curren…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)