Modeling and Optimizing Task Selection for Better Transfer in Contextual Reinforcement Learning

This paper introduces an approach combining model-based transfer learning with contextual reinforcement learning to improve knowledge transfer between environments. At its core, the method learns reusable environment dynamics while adapting to context-specific variations.

The key technical components:

• Contextual model architecture that separates shared and context-specific features
• Transfer learning mechanism that identifies and preserves core dynamics
• Exploration strategy balancing known vs novel behaviors
• Sample-efficient training through model reuse across contexts

Results show significant improvements over baselines:

• 40% reduction in samples needed for new environment adaptation
• Better asymptotic performance on complex navigation tasks
• More stable learning curves across different contexts
• Effective transfer even with substantial environment variations

I think this approach could be particularly valuable for robotics applications where training data is expensive and environments vary frequently. The separation of shared vs specific dynamics feels like a natural way to decompose the transfer learning problem.

That said, I’m curious about the computational overhead – modeling environment dynamics isn’t cheap, and the paper doesn’t deeply analyze this tradeoff. I’d also like to see testing on a broader range of domains to better understand where this approach works best.

TLDR: Combines model-based methods with contextual RL to enable efficient knowledge transfer between environments. Shows 40% better sample efficiency and improved performance through reusable dynamics modeling.

Full summary is here. Paper here.