Summary of Stable Continual Reinforcement Learning Via Diffusion-based Trajectory Replay, by Feng Chen et al.
Stable Continual Reinforcement Learning via Diffusion-based Trajectory Replay
by Feng Chen, Fuguang Han, Cong Guan, Lei Yuan, Zhilong Zhang, Yang Yu, Zongzhang Zhang
First submitted to arxiv on: 16 Nov 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 In this paper, researchers explore the challenge of catastrophic forgetting in continual Reinforcement Learning (RL) and propose a novel algorithm called DISTR. The goal is to equip agents with the ability to address sequential decision-making tasks without forgetting past knowledge. To achieve this, they introduce a diffusion model that memorizes high-return trajectory distributions for each task and wakes them up during policy learning on new tasks. A prioritization mechanism is also proposed to selectively replay trajectories from pivotal tasks. The algorithm is tested on the Continual World benchmark, demonstrating a balance between stability and plasticity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are trying to make computers better at learning new things without forgetting what they already know. They want to teach these computers to learn step-by-step, without getting confused or losing their memory. The researchers come up with a new way of doing this using something called diffusion models, which help the computer remember important information from past tasks. This allows the computer to improve its learning over time, making it better at solving problems. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Reinforcement learning
