# Hyperparameter Optimisation with Practical Interpretability and Explanation Methods in Probabilistic Curriculum Learning

arXiv: https://arxiv.org/abs/2504.06683 · HTML: https://mlsanity.com/paper/2504.06683/

## 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 16 of its 29 references that are in the map.
- First posted on arXiv 9 Apr 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, 16 of 16)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance](https://mlsanity.com/paper/2304.11127/) (2023)
- [Hyperparameter Selection for Offline Reinforcement Learning](https://mlsanity.com/paper/2007.09055/) (2020)
- [Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction](https://mlsanity.com/paper/1906.03631/) (2019)
- [Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control](https://mlsanity.com/paper/1708.04133/) (2017)
- [On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning](https://mlsanity.com/paper/2102.13651/) (2021)
- [Smooth Exploration for Robotic Reinforcement Learning](https://mlsanity.com/paper/2005.05719/) (2020)
- [Explanation of Machine Learning Models Using Shapley Additive Explanation and Application for Real Data in Hospital](https://mlsanity.com/paper/2112.11071/) (2021)
- [Parameter Optimization and Learning in a Spiking Neural Network for UAV Obstacle Avoidance targeting Neuromorphic Processors](https://mlsanity.com/paper/1910.07960/) (2019)
- [Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation](https://mlsanity.com/paper/2106.10950/) (2021)
- [Probabilistic Curriculum Learning for Goal-Based Reinforcement Learning](https://mlsanity.com/paper/2504.01459/) (2025)

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

- [GRPO Training Dynamics for Small Language Models](https://mlsanity.com/paper/2609.39321/) (2026)

## Abstract (arXiv)

Hyperparameter optimisation (HPO) is crucial for achieving strong performance in reinforcement learning (RL), as RL algorithms are inherently sensitive to hyperparameter settings. Probabilistic Curriculum Learning (PCL) is a curriculum learning strategy designed to improve RL performance by structuring the agent's learning process, yet effective hyperparameter tuning remains challenging and computationally demanding. In this paper, we provide an empirical analysis of hyperparameter interactions and their effects on the performance of a PCL algorithm within standard RL tasks, including point-maze navigation and DC motor control. Using the AlgOS framework integrated with Optuna's Tree-Structured Parzen Estimator (TPE), we present strategies to refine hyperparameter search spaces, enhancing optimisation efficiency. Additionally, we introduce a novel SHAP-based interpretability approach tailored specifically for analysing hyperparameter impacts, offering clear insights into how individual hyperparameters and their interactions influence RL performance. Our work contributes practical guidelines and interpretability tools that significantly improve the effectiveness and computational feasibility of hyperparameter optimisation in reinforcement learning.