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openai.com·13h ago
AI Research
arXiv CS.AI·Jun 12·5 min read
ARROW: Augmented Replay for RObust World models
TL;DR
Continual reinforcement learning faces the challenge of retaining previously learned skills while acquiring new ones, often leading to catastrophic forgetting. ARROW (Augmented Replay for RObust World models) is a model-based algorithm that enhances DreamerV3 by introducing a memory-efficient replay buffer with short-term and long-term components.
✦ Why It Matters
Engineers can leverage ARROW's approach to improve memory efficiency in reinforcement learning applications.
Key Takeaways
- Explore model-based approaches for continual reinforcement learning.
- Implement memory-efficient replay buffers in RL systems.
- Utilize short-term and long-term experience sampling strategies.
- Investigate bio-inspired methods to enhance learning retention.
Full Summary
Continual reinforcement learning (RL) aims to enable agents to learn new tasks without forgetting previously acquired skills, a problem known as catastrophic forgetting. ARROW, which stands for Augmented Replay for RObust World models, builds on the DreamerV3 framework by implementing a novel replay buffer that is both memory-efficient and capable of matching experience distributions.
This buffer consists of a short-term component for recent experiences and a long-term component that intelligently samples diverse tasks. The researchers tested ARROW in two challenging environments: Atari games, which lack shared structure, and Procgen CoinRun variants, which allow for knowledge transfer.
Results indicated that ARROW outperformed both model-free and model-based baselines, showing significantly less forgetting in the Atari tasks while achieving comparable performance in knowledge transfer tasks. These findings suggest that model-based approaches, inspired by neuroscience, can enhance continual learning in RL, opening avenues for further research and application.