Online Reinforcement Learning with Passive Memory
On the map: Deep reinforcement learning methods → Deep RL algorithms and representations
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 32 references that are in the map. First posted on arXiv 18 Oct 2024.
Builds on
10 of 32 references.
References · 10
- 2017Proximal Policy Optimization Algorithmscited by 14,592
- 2015Continuous control with deep reinforcement learningcited by 3,290
- 2015Trust Region Policy Optimizationcited by 2,513
- 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,889
- 2015Deep Reinforcement Learning with Double Q-learningcited by 1,710
- 2023Efficient Online Reinforcement Learning with Offline Datacited by 334
- 2019AlgaeDICE: Policy Gradient from Arbitrary Experiencecited by 188
- 2022Hybrid RL: Using Both Offline and Online Data Can Make RL Efficientcited by 159
- 2020Reinforcement Learning via Fenchel-Rockafellar Dualitycited by 100
- 2022Offline Reinforcement Learning Under Value and Density-Ratio Realizability: The Power of Gapscited by 34
Built on by
Citing papers per year: 2025: 1.
1 paper.