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

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.

submitted by /u/Successful-Western27
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