Exploration in Reinforcement Learning Reading List

Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated April 2026

How agents learn to explore efficiently in large, sparse-reward environments.

1. Unifying Count-Based Exploration and Intrinsic Motivation
   Bellemare et al.. NeurIPS 2016.
2. Curiosity-Driven Exploration by Self-Supervised Prediction
   Pathak et al.. ICML 2017.
3. Never Give Up: Learning Directed Exploration Strategies
   Badia et al.. ICLR 2020.
4. First Return, Then Explore
   Ecoffet et al.. Nature 2021.
5. Exploration by Random Network Distillation
   Burda et al.. ICLR 2019.
6. Self-Supervised Exploration via Disagreement
   Pathak et al.. ICML 2019.
7. BYOL-Explore: Exploration by Bootstrapped Prediction
   Guo et al.. NeurIPS 2022.
8. Asymptotic Convergence and Performance of Multi-Agent Q-Learning Dynamics
   Hussain, Belardinelli, Piliouras. ICML 2023.