Core Concepts
Exploring strategies for effective offline-to-online reinforcement learning.
Abstract
The content discusses the paradigm of offline pretraining followed by online fine-tuning in reinforcement learning. It introduces the concept of planning to go out-of-distribution (PTGOOD) to improve exploration in the offline-to-online setting. The study evaluates various exploration methods and introduces PTGOOD as a solution to enhance agent returns during online fine-tuning.
Directory:
- Abstract
- Offline-to-online (OtO) paradigm for reinforcement learning.
- Introducing PTGOOD for improved exploration.
- Introduction
- Value of offline training with a static dataset.
- Importance of fine-tuning over limited online interactions.
- Related Work
- Exploration strategies in reinforcement learning.
- Offline RL methods and OtO RL research.
- Online Exploration Methods
- Intrinsic rewards and UCB exploration in the OtO setting.
- Issues with existing exploration methods.
- Planning to Go Out-of-Distribution
- Introduction of PTGOOD algorithm.
- Utilizing Conditional Entropy Bottleneck for exploration.
- Experiments
- Evaluation of PTGOOD and baselines in various environments.
- Comparison of different exploration strategies.
- Conclusion
- Significance of PTGOOD in improving exploration and agent returns.
Stats
Offline pretraining with a static dataset followed by online fine-tuning (offline-to-online, or OtO) is a paradigm well matched to a real-world RL deployment process.
PTGOOD significantly improves agent returns during online fine-tuning and avoids suboptimal policy convergence in several environments.
Quotes
"Intrinsic rewards add training instability through reward-function modification."
"UCB methods are myopic and it is unclear which learned-component’s ensemble to use for action selection."