# Accuracy-based Curriculum Learning in Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1806.09614 · HTML: https://mlsanity.com/paper/1806.09614/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2018.

## Built on by (most cited first, 15 of 15)

- [Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases](https://mlsanity.com/paper/2102.11965/) (2021)
- [Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation](https://mlsanity.com/paper/2210.10195/) (2022)
- [A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning](https://mlsanity.com/paper/2102.13176/) (2021)
- [Domain Randomization via Entropy Maximization](https://mlsanity.com/paper/2311.01885/) (2023)
- [On Efficient Reinforcement Learning for Full-length Game of StarCraft II](https://mlsanity.com/paper/2209.11553/) (2022)
- [Robust Reinforcement Learning via Genetic Curriculum](https://mlsanity.com/paper/2202.08393/) (2022)
- [Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills](https://mlsanity.com/paper/2310.19424/) (2023)
- [MAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces](https://mlsanity.com/paper/2502.07709/) (2025)
- [Investigating Value of Curriculum Reinforcement Learning in Autonomous Driving Under Diverse Road and Weather Conditions](https://mlsanity.com/paper/2103.07903/) (2021)
- [Efficient Skill Discovery via Regret-Aware Optimization](https://mlsanity.com/paper/2506.21044/) (2025)
- [CRISP: Curriculum Inducing Primitive Informed Subgoal Prediction for Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2304.03535/) (2023)
- [Maneuver Decision-Making For Autonomous Air Combat Through Curriculum Learning And Reinforcement Learning With Sparse Rewards](https://mlsanity.com/paper/2302.05838/) (2023)
- [WINFlowNets: Warm-up Integrated Networks Training of Generative Flow Networks for Robotics and Machine Fault Adaptation](https://mlsanity.com/paper/2603.17301/) (2026)
- [Self Paced Gaussian Contextual Reinforcement Learning](https://mlsanity.com/paper/2603.23755/) (2026)
- [Scenario Generation for Risk-Aware Reinforcement Learning with Probably Approximately Safe Guarantees](https://mlsanity.com/paper/2606.04812/) (2026)

## Abstract (arXiv)

In this paper, we investigate a new form of automated curriculum learning based on adaptive selection of accuracy requirements, called accuracy-based curriculum learning. Using a reinforcement learning agent based on the Deep Deterministic Policy Gradient algorithm and addressing the Reacher environment, we first show that an agent trained with various accuracy requirements sampled randomly learns more efficiently than when asked to be very accurate at all times. Then we show that adaptive selection of accuracy requirements, based on a local measure of competence progress, automatically generates a curriculum where difficulty progressively increases, resulting in a better learning efficiency than sampling randomly.