# Self Paced Gaussian Contextual Reinforcement Learning

arXiv: https://arxiv.org/abs/2603.23755 · HTML: https://mlsanity.com/paper/2603.23755/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 21 of 21)

- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables](https://mlsanity.com/paper/1903.08254/) (2019)
- [Automatic Goal Generation for Reinforcement Learning Agents](https://mlsanity.com/paper/1705.06366/) (2017)
- [Contextual Markov Decision Processes](https://mlsanity.com/paper/1502.02259/) (2015)
- [Reverse Curriculum Generation for Reinforcement Learning](https://mlsanity.com/paper/1707.05300/) (2017)
- [Automatic Curriculum Learning For Deep RL: A Short Survey](https://mlsanity.com/paper/2003.04664/) (2020)
- [Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments](https://mlsanity.com/paper/1910.07224/) (2019)
- [Self-Paced Deep Reinforcement Learning](https://mlsanity.com/paper/2004.11812/) (2020)
- [Divide-and-Conquer Reinforcement Learning](https://mlsanity.com/paper/1711.09874/) (2017)
- [Contextualize Me -- The Case for Context in Reinforcement Learning](https://mlsanity.com/paper/2202.04500/) (2022)
- [Environment Generation for Zero-Shot Compositional Reinforcement Learning](https://mlsanity.com/paper/2201.08896/) (2022)
- [Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation](https://mlsanity.com/paper/2210.10195/) (2022)
- [Self-Paced Contextual Reinforcement Learning](https://mlsanity.com/paper/1910.02826/) (2019)
- [Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems](https://mlsanity.com/paper/2111.04613/) (2021)
- [Self-Paced Context Evaluation for Contextual Reinforcement Learning](https://mlsanity.com/paper/2106.05110/) (2021)
- [CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning](https://mlsanity.com/paper/2110.02102/) (2021)
- [Understanding the Complexity Gains of Single-Task RL with a Curriculum](https://mlsanity.com/paper/2212.12809/) (2022)
- [Accuracy-based Curriculum Learning in Deep Reinforcement Learning](https://mlsanity.com/paper/1806.09614/) (2018)
- [Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts](https://mlsanity.com/paper/2403.06966/) (2024)
- [Proximal Curriculum with Task Correlations for Deep Reinforcement Learning](https://mlsanity.com/paper/2405.02481/) (2024)
- [Neural Auto-Curricula](https://mlsanity.com/paper/2106.02745/) (2021)

## Abstract (arXiv)

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.