Self Paced Gaussian Contextual Reinforcement Learning
arXiv:2603.23755v1 Announce Type: new 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. We provide theoretical guarantees on convergence and validate our method across several contextual RL benchmarks, including the Point Mass, Lunar Lander, and Ball Catching environments. Experimental results show that SPGL matches or outperforms existing curriculum methods, especially in hidden context scenarios, and achiev
arXiv:2603.23755v1 Announce Type: new 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. We provide theoretical guarantees on convergence and validate our method across several contextual RL benchmarks, including the Point Mass, Lunar Lander, and Ball Catching environments. Experimental results show that SPGL matches or outperforms existing curriculum methods, especially in hidden context scenarios, and achieves more stable context distribution convergence. Our method offers a scalable, principled alternative for curriculum generation in challenging continuous and partially observable domains.
Executive Summary
Self-Paced Gaussian Contextual Reinforcement Learning (SPGL) is a novel approach to curriculum learning that leverages a closed-form update rule for Gaussian context distributions. This method maintains the efficiency and adaptability of traditional self-paced methods while significantly reducing computational overhead. SPGL is validated through several contextual RL benchmarks and outperforms existing curriculum methods in hidden context scenarios. The paper provides theoretical guarantees on convergence and offers a scalable, principled alternative for curriculum generation in challenging continuous and partially observable domains. The proposed method is a significant advancement in the field of reinforcement learning, particularly in the context of high-dimensional spaces.
Key Points
- ▸ SPGL introduces a closed-form update rule for Gaussian context distributions, avoiding costly numerical procedures.
- ▸ The method maintains the efficiency and adaptability of traditional self-paced methods.
- ▸ SPGL is validated across several contextual RL benchmarks, including Point Mass, Lunar Lander, and Ball Catching environments.
Merits
Scalability
SPGL significantly reduces computational overhead, making it a more scalable approach for high-dimensional context spaces.
Efficiency
The method maintains the efficiency of traditional self-paced methods while improving adaptability in challenging domains.
Principled alternative
SPGL offers a principled approach to curriculum generation, providing theoretical guarantees on convergence and adaptability.
Demerits
Limited scope
The paper focuses primarily on contextual RL benchmarks and may not generalize to other domains or applications.
Dependence on Gaussian distributions
The method relies on Gaussian context distributions, which may not be suitable for all types of context spaces.
Expert Commentary
The paper represents a significant advancement in the field of reinforcement learning, particularly in the context of high-dimensional spaces. The proposed method addresses critical issues related to scalability and efficiency, offering a principled approach to curriculum generation. However, the paper's scope is limited to contextual RL benchmarks, and further research is needed to generalize the findings to other domains. Additionally, the reliance on Gaussian distributions may limit the method's applicability in certain contexts. Nevertheless, the paper's contributions are substantial, and its implications for both practical applications and policy-making are significant.
Recommendations
- ✓ Future research should focus on extending the scope of SPGL to other domains and applications, exploring its applicability in non-contextual RL settings.
- ✓ The authors should investigate alternative distribution families to broaden the method's applicability and improve its robustness in diverse context spaces.
Sources
Original: arXiv - cs.LG
