# Exploration and Anti-Exploration with Distributional Random Network Distillation

arXiv: https://arxiv.org/abs/2401.09750 · HTML: https://mlsanity.com/paper/2401.09750/

## Key facts

- Cited by 33 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 40 of its 49 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## 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, 30 of 40)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [D4RL: Datasets for Deep Data-Driven Reinforcement Learning](https://mlsanity.com/paper/2004.07219/) (2020)
- [Offline Reinforcement Learning with Implicit Q-Learning](https://mlsanity.com/paper/2110.06169/) (2021)
- [Off-Policy Deep Reinforcement Learning without Exploration](https://mlsanity.com/paper/1812.02900/) (2018)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Exploration by Random Network Distillation](https://mlsanity.com/paper/1810.12894/) (2018)
- [A Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06860/) (2021)
- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/1709.10087/) (2017)
- [Behavior Regularized Offline Reinforcement Learning](https://mlsanity.com/paper/1911.11361/) (2019)
- [Unifying Count-Based Exploration and Intrinsic Motivation](https://mlsanity.com/paper/1606.01868/) (2016)
- [Deep Exploration via Bootstrapped DQN](https://mlsanity.com/paper/1602.04621/) (2016)
- [Minimax Regret Bounds for Reinforcement Learning](https://mlsanity.com/paper/1703.05449/) (2017)
- [#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning](https://mlsanity.com/paper/1611.04717/) (2016)
- [Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble](https://mlsanity.com/paper/2110.01548/) (2021)
- [Large-Scale Study of Curiosity-Driven Learning](https://mlsanity.com/paper/1808.04355/) (2018)
- [Critic Regularized Regression](https://mlsanity.com/paper/2006.15134/) (2020)
- [Bellman-consistent Pessimism for Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06926/) (2021)
- [Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research](https://mlsanity.com/paper/1802.09464/) (2018)
- [Count-Based Exploration with Neural Density Models](https://mlsanity.com/paper/1703.01310/) (2017)
- [Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics](https://mlsanity.com/paper/2005.04269/) (2020)
- [Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models](https://mlsanity.com/paper/1507.00814/) (2015)
- [Revisiting the Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2305.09836/) (2023)
- [CORL: Research-oriented Deep Offline Reinforcement Learning Library](https://mlsanity.com/paper/2210.07105/) (2022)
- [Count-Based Exploration with the Successor Representation](https://mlsanity.com/paper/1807.11622/) (2018)
- [Mildly Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2206.04745/) (2022)
- [RORL: Robust Offline Reinforcement Learning via Conservative Smoothing](https://mlsanity.com/paper/2206.02829/) (2022)
- [Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning](https://mlsanity.com/paper/1703.01732/) (2017)

## Built on by (most cited first, 30 of 33)

- [Maximum Likelihood Reinforcement Learning](https://mlsanity.com/paper/2602.02710/) (2026)
- [ODRL: A Benchmark for Off-Dynamics Reinforcement Learning](https://mlsanity.com/paper/2410.20750/) (2024)
- [Highly Efficient Self-Adaptive Reward Shaping for Reinforcement Learning](https://mlsanity.com/paper/2408.03029/) (2024)
- [SEABO: A Simple Search-Based Method for Offline Imitation Learning](https://mlsanity.com/paper/2402.03807/) (2024)
- [Risk-Sensitive RL for Alleviating Exploration Dilemmas in Large Language Models](https://mlsanity.com/paper/2509.24261/) (2025)
- [EntroPIC: Towards Stable Long-Term Training of LLMs via Entropy Stabilization with Proportional-Integral Control](https://mlsanity.com/paper/2511.15248/) (2025)
- [World Models with Hints of Large Language Models for Goal Achieving](https://mlsanity.com/paper/2406.07381/) (2024)
- [FastFT: Accelerating Reinforced Feature Transformation via Advanced Exploration Strategies](https://mlsanity.com/paper/2503.20394/) (2025)
- [Deep Exploration with PAC-Bayes](https://mlsanity.com/paper/2402.03055/) (2024)
- [Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2412.15517/) (2024)
- [FOSP: Fine-tuning Offline Safe Policy through World Models](https://mlsanity.com/paper/2407.04942/) (2024)
- [The Role of Deep Learning Regularizations on Actors in Offline RL](https://mlsanity.com/paper/2409.07606/) (2024)
- [Overcoming Non-stationary Dynamics with Evidential Proximal Policy Optimization](https://mlsanity.com/paper/2503.01468/) (2025)
- [Beyond Noisy-TVs: Noise-Robust Exploration Via Learning Progress Monitoring](https://mlsanity.com/paper/2509.25438/) (2025)
- [Finite-time Convergence Analysis of Actor-Critic with Evolving Reward](https://mlsanity.com/paper/2510.12334/) (2025)
- [Temporal Difference Learning with Constrained Initial Representations](https://mlsanity.com/paper/2602.11800/) (2026)
- [Novelty-based Sample Reuse for Continuous Robotics Control](https://mlsanity.com/paper/2410.13490/) (2024)
- [Exploration by Random Distribution Distillation](https://mlsanity.com/paper/2505.11044/) (2025)
- [Exploration by Random Reward Perturbation](https://mlsanity.com/paper/2506.08737/) (2025)
- [Offline Reinforcement Learning with Penalized Action Noise Injection](https://mlsanity.com/paper/2507.02356/) (2025)
- [From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning](https://mlsanity.com/paper/2507.12815/) (2025)
- [Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning](https://mlsanity.com/paper/2507.15287/) (2025)
- [Learning Without Time-Based Embodiment Resets in Soft-Actor Critic](https://mlsanity.com/paper/2512.06252/) (2025)
- [Cross-Domain Offline Policy Adaptation via Selective Transition Correction](https://mlsanity.com/paper/2602.05776/) (2026)
- [Debiased Model-based Representations for Sample-efficient Continuous Control](https://mlsanity.com/paper/2605.11711/) (2026)
- [CDSA: Conservative Denoising Score-based Algorithm for Offline Reinforcement Learning](https://mlsanity.com/paper/2406.07541/) (2024)
- [Coupled Distributional Random Expert Distillation for World Model Online Imitation Learning](https://mlsanity.com/paper/2505.02228/) (2025)
- [ObjectRL: An Object-Oriented Reinforcement Learning Codebase](https://mlsanity.com/paper/2507.03487/) (2025)
- [CDE: Concept-Driven Exploration for Reinforcement Learning](https://mlsanity.com/paper/2510.08851/) (2025)
- [Distributional Active Inference](https://mlsanity.com/paper/2601.20985/) (2026)

## Abstract (arXiv)

Exploration remains a critical issue in deep reinforcement learning for an agent to attain high returns in unknown environments. Although the prevailing exploration Random Network Distillation (RND) algorithm has been demonstrated to be effective in numerous environments, it often needs more discriminative power in bonus allocation. This paper highlights the "bonus inconsistency" issue within RND, pinpointing its primary limitation. To address this issue, we introduce the Distributional RND (DRND), a derivative of the RND. DRND enhances the exploration process by distilling a distribution of random networks and implicitly incorporating pseudo counts to improve the precision of bonus allocation. This refinement encourages agents to engage in more extensive exploration. Our method effectively mitigates the inconsistency issue without introducing significant computational overhead. Both theoretical analysis and experimental results demonstrate the superiority of our approach over the original RND algorithm. Our method excels in challenging online exploration scenarios and effectively serves as an anti-exploration mechanism in D4RL offline tasks. Our code is publicly available at https://github.com/yk7333/DRND.