Self Paced Gaussian Contextual Reinforcement Learning
Summary: arXiv:2603.23755v1 Announce Type: cross
Abstract
Curriculum learning improves reinforcement learning (RL) efficiency by sequencing tasks from simple to complex. However, many self-paced curriculum methods rely on computationally expensive inner-loop optimizations, limiting their scalability in high-dimensional context spaces. In this paper, we propose Self-Paced Gaussian Curriculum Learning (SPGL), a novel approach that avoids costly numerical procedures by leveraging a closed-form update rule for Gaussian context distributions. SPGL maintains the sample efficiency and adaptability of traditional self-paced methods while substantially reducing computational overhead.
Theoretical Framework
We provide theoretical guarantees on convergence and validate our method across several contextual RL benchmarks, including:
- Point Mass
- Lunar Lander
- Ball Catching
Our theoretical framework establishes the conditions under which SPGL effectively converges towards optimal policies, offering a robust foundation for its application in real-world scenarios.
Experimental Validation
The experimental results demonstrate that SPGL matches or outperforms existing curriculum methods, particularly in hidden context scenarios. The findings illustrate the following key advantages of SPGL:
- Sample Efficiency: SPGL preserves the sample efficiency characteristic of traditional self-paced learning approaches.
- Reduced Computational Overhead: By eliminating the need for costly numerical procedures, SPGL allows for faster training cycles.
- Stable Convergence: Our method achieves more stable context distribution convergence, which is crucial for applications in complex environments.
Applications and Future Work
The scalability and efficiency of SPGL make it a promising candidate for various applications, especially in continuous and partially observable domains. Potential areas of exploration include:
- Robotics, where efficient learning from high-dimensional sensory inputs is essential.
- Game AI, where rapid adaptation to complex environments can enhance performance.
- Healthcare, particularly in personalized treatment plans where contextual factors are numerous and varied.
Future research will focus on further refining the SPGL methodology, exploring its applicability to other reinforcement learning paradigms, and integrating it with emerging technologies such as deep learning for even greater efficiency and effectiveness.
Conclusion
Self-Paced Gaussian Curriculum Learning represents a significant advancement in the field of reinforcement learning, providing a scalable and principled alternative for curriculum generation. By combining theoretical guarantees with practical performance, SPGL paves the way for more efficient and effective learning in complex domains.