Summary of Augmenting Replay in World Models For Continual Reinforcement Learning, by Luke Yang et al.
Augmenting Replay in World Models for Continual Reinforcement Learning
by Luke Yang, Levin Kuhlmann, Gideon Kowadlo
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 As machine learning educators writing for a technical audience, we can summarize the abstract as follows: The paper introduces WMAR (World Models with Augmented Replay), a novel model-based RL algorithm that leverages knowledge of the environment and employs a memory-efficient distribution-matching replay buffer. Compared to DreamerV3, which uses a simple FIFO buffer, WMAR demonstrates favourable properties for continual RL in tasks with shared structure using OpenAI Procgen and without shared structure using the Atari benchmark. The results show slight benefits in terms of forgetting characteristics on past and future tasks for tasks with shared structure, while WMAR outperforms DreamerV3 substantially in tasks without shared structure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Continual learning allows machines to learn new skills without forgetting old ones. Researchers have been working on this problem using different approaches, but most struggle with memory requirements making them hard to scale. This paper introduces a new way called WMAR that’s inspired by how our brains work. It uses a model of the world and a special replay buffer that helps it learn quickly and remember things well. They tested WMAR and another approach called DreamerV3 on different tasks and found that WMAR performed better, especially when learning new skills didn’t share any similarities with old ones. |
Keywords
* Artificial intelligence * Continual learning * Machine learning
