# Never Give Up: Learning Directed Exploration Strategies

arXiv: https://arxiv.org/abs/2002.06038 · HTML: https://mlsanity.com/paper/2002.06038/

## Key facts

- Cited by 212 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2020.

## Built on by (most cited first, 30 of 212)

- [Behavior From the Void: Unsupervised Active Pre-Training](https://mlsanity.com/paper/2103.04551/) (2021)
- [Exploration in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2205.00824/) (2022)
- [APS: Active Pretraining with Successor Features](https://mlsanity.com/paper/2108.13956/) (2021)
- [State Entropy Maximization with Random Encoders for Efficient Exploration](https://mlsanity.com/paper/2102.09430/) (2021)
- [Celebrating Diversity in Shared Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2106.02195/) (2021)
- [METRA: Scalable Unsupervised RL with Metric-Aware Abstraction](https://mlsanity.com/paper/2310.08887/) (2023)
- [BYOL-Explore: Exploration by Bootstrapped Prediction](https://mlsanity.com/paper/2206.08332/) (2022)
- [Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain](https://mlsanity.com/paper/2109.06668/) (2021)
- [Semantic Exploration from Language Abstractions and Pretrained Representations](https://mlsanity.com/paper/2204.05080/) (2022)
- [Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration](https://mlsanity.com/paper/2111.11032/) (2021)
- [DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization](https://mlsanity.com/paper/2310.19668/) (2023)
- [Efficient Exploration for LLMs](https://mlsanity.com/paper/2402.00396/) (2024)
- [Paxion: Patching Action Knowledge in Video-Language Foundation Models](https://mlsanity.com/paper/2305.10683/) (2023)
- [MADE: Exploration via Maximizing Deviation from Explored Regions](https://mlsanity.com/paper/2106.10268/) (2021)
- [Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality](https://mlsanity.com/paper/2205.13521/) (2022)
- [Multi-agent Reinforcement Learning: A Comprehensive Survey](https://mlsanity.com/paper/2312.10256/) (2023)
- [Geometric Entropic Exploration](https://mlsanity.com/paper/2101.02055/) (2021)
- [Thompson sampling for improved exploration in GFlowNets](https://mlsanity.com/paper/2306.17693/) (2023)
- [OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments](https://mlsanity.com/paper/2306.08649/) (2023)
- [Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments](https://mlsanity.com/paper/2101.08152/) (2021)
- [A Max-Min Entropy Framework for Reinforcement Learning](https://mlsanity.com/paper/2106.10517/) (2021)
- [Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey](https://mlsanity.com/paper/2110.01411/) (2021)
- [Human-level Atari 200x faster](https://mlsanity.com/paper/2209.07550/) (2022)
- [An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey](https://mlsanity.com/paper/2209.08890/) (2022)
- [Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning](https://mlsanity.com/paper/2306.03186/) (2023)
- [How to Stay Curious while Avoiding Noisy TVs using Aleatoric Uncertainty Estimation](https://mlsanity.com/paper/2102.04399/) (2021)
- [Synthetic Returns for Long-Term Credit Assignment](https://mlsanity.com/paper/2102.12425/) (2021)
- [Principled Exploration via Optimistic Bootstrapping and Backward Induction](https://mlsanity.com/paper/2105.06022/) (2021)
- [Reward Models in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2506.15421/) (2025)
- [Adaptive Rational Activations to Boost Deep Reinforcement Learning](https://mlsanity.com/paper/2102.09407/) (2021)

## Abstract (arXiv)

We propose a reinforcement learning agent to solve hard exploration games by learning a range of directed exploratory policies. We construct an episodic memory-based intrinsic reward using k-nearest neighbors over the agent's recent experience to train the directed exploratory policies, thereby encouraging the agent to repeatedly revisit all states in its environment. A self-supervised inverse dynamics model is used to train the embeddings of the nearest neighbour lookup, biasing the novelty signal towards what the agent can control. We employ the framework of Universal Value Function Approximators (UVFA) to simultaneously learn many directed exploration policies with the same neural network, with different trade-offs between exploration and exploitation. By using the same neural network for different degrees of exploration/exploitation, transfer is demonstrated from predominantly exploratory policies yielding effective exploitative policies. The proposed method can be incorporated to run with modern distributed RL agents that collect large amounts of experience from many actors running in parallel on separate environment instances. Our method doubles the performance of the base agent in all hard exploration in the Atari-57 suite while maintaining a very high score across the remaining games, obtaining a median human normalised score of 1344.0%. Notably, the proposed method is the first algorithm to achieve non-zero rewards (with a mean score of 8,400) in the game of Pitfall! without using demonstrations or hand-crafted features.