# Automating Curriculum Learning for Reinforcement Learning using a Skill-Based Bayesian Network

arXiv: https://arxiv.org/abs/2502.15662 · HTML: https://mlsanity.com/paper/2502.15662/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## 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, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks](https://mlsanity.com/paper/2306.13831/) (2023)
- [Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design](https://mlsanity.com/paper/2012.02096/) (2020)
- [Modular Multitask Reinforcement Learning with Policy Sketches](https://mlsanity.com/paper/1611.01796/) (2016)
- [Evolving Curricula with Regret-Based Environment Design](https://mlsanity.com/paper/2203.01302/) (2022)
- [Replay-Guided Adversarial Environment Design](https://mlsanity.com/paper/2110.02439/) (2021)
- [Hierarchical Imitation and Reinforcement Learning](https://mlsanity.com/paper/1803.00590/) (2018)
- [Leveraging Human Guidance for Deep Reinforcement Learning Tasks](https://mlsanity.com/paper/1909.09906/) (2019)
- [Practice Makes Perfect: Planning to Learn Skill Parameter Policies](https://mlsanity.com/paper/2402.15025/) (2024)
- [Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer](https://mlsanity.com/paper/1710.03850/) (2017)
- [Learn and Link: Learning Critical Regions for Efficient Planning](https://mlsanity.com/paper/1903.03258/) (2019)
- [FORK: A Forward-Looking Actor For Model-Free Reinforcement Learning](https://mlsanity.com/paper/2010.01652/) (2020)

## Built on by (most cited first, 1 of 1)

- [Active Curriculum Refinement for Reinforcement Learning](https://mlsanity.com/paper/2608.26469/) (2026)

## Abstract (arXiv)

A major challenge for reinforcement learning is automatically generating curricula to reduce training time or improve performance in some target task. We introduce SEBNs (Skill-Environment Bayesian Networks) which model a probabilistic relationship between a set of skills, a set of goals that relate to the reward structure, and a set of environment features to predict policy performance on (possibly unseen) tasks. We develop an algorithm that uses the inferred estimates of agent success from SEBN to weigh the possible next tasks by expected improvement. We evaluate the benefit of the resulting curriculum on three environments: a discrete gridworld, continuous control, and simulated robotics. The results show that curricula constructed using SEBN frequently outperform other baselines.