Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration
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Abstract (arXiv)
Curiosity-based reward schemes can present powerful exploration mechanisms which facilitate the discovery of solutions for complex, sparse or long-horizon tasks. However, as the agent learns to reach previously unexplored spaces and the objective adapts to reward new areas, many behaviours emerge only to disappear due to being overwritten by the constantly shifting objective. We argue that merely using curiosity for fast environment exploration or as a bonus reward for a specific task does not harness the full potential of this technique and misses useful skills. Instead, we propose to shift the focus towards retaining the behaviours which emerge during curiosity-based learning. We posit that these self-discovered behaviours serve as valuable skills in an agent's repertoire to solve related tasks. Our experiments demonstrate the continuous shift in behaviour throughout training and the benefits of a simple policy snapshot method to reuse discovered behaviour for transfer tasks.
Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 25 of its 41 references that are in the map. First posted on arXiv 17 Sep 2021.
Builds on
25 of 41 references.
References · 25
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,194
- 2018Exploration by Random Network Distillationcited by 993
- 2018Diversity is All You Need: Learning Skills without a Reward Functioncited by 752
- 2016Unifying Count-Based Exploration and Intrinsic Motivationcited by 639
- 2020Planning to Explore via Self-Supervised World Modelscited by 368
- 2018Maximum a Posteriori Policy Optimisationcited by 328
- 2019Learning Latent Plans from Playcited by 316
- 2019Dynamics-Aware Unsupervised Discovery of Skillscited by 313
- 2018Large-Scale Study of Curiosity-Driven Learningcited by 280
- 2016Variational Intrinsic Controlcited by 267
- 2019Self-Supervised Exploration via Disagreementcited by 265
13 more
- 2018Learning by Playing - Solving Sparse Reward Tasks from Scratchcited by 167
- 2015Variational Information Maximisation for Intrinsically Motivated Reinforcement Learningcited by 144
- 2017Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Playcited by 141
- 2018Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Controlcited by 113
- 2017Surprise-Based Intrinsic Motivation for Deep Reinforcement Learningcited by 83
- 2016Loss is its own Reward: Self-Supervision for Reinforcement Learningcited by 72
- 2021Asymmetric self-play for automatic goal discovery in robotic manipulationcited by 72
- 2018Learning to Play with Intrinsically-Motivated Self-Aware Agentscited by 35
- 2020Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learningcited by 31
- 2020Data-efficient Hindsight Off-policy Option Learningcited by 31
- 2019Learning Gentle Object Manipulation with Curiosity-Driven Deep Reinforcement Learningcited by 26
- 2020Representation Matters: Improving Perception and Exploration for Roboticscited by 11
- 2020Simple Sensor Intentions for Explorationcited by 5
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Citing papers per year: 2022: 6; 2023: 4; 2024: 2; 2025: 1; 2026: 1.
14 papers.
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- 2022BYOL-Explore: Exploration by Bootstrapped Predictioncited by 73
- 2023Towards A Unified Agent with Foundation Modelscited by 47
- 2025LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilitiescited by 41
- 2024Data-driven Discovery with Large Generative Modelscited by 34
- 2022Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulationcited by 31
- 2022The Challenges of Exploration for Offline Reinforcement Learningcited by 26
- 2023Regularity as Intrinsic Reward for Free Playcited by 5
- 2022Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environmentscited by 4
- 2023Intrinsic Motivation in Model-based Reinforcement Learning: A Brief Reviewcited by 3
- 2024Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learningcited by 3
- 2022A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement Learningcited by 2
- 2023The Construction of Reality in an AI: A Reviewcited by 1
- 2026What You Think is What You See: Driving Exploration in VLM Agents via Visual-Linguistic Curiosity5 May 2026
- 2025LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities22 Apr 2025
- 2024Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learning27 May 2024
- 2024Data-driven Discovery with Large Generative Models21 Feb 2024
- 2023Regularity as Intrinsic Reward for Free Play3 Dec 2023
- 2023Towards A Unified Agent with Foundation Models18 Jul 2023
- 2023The Construction of Reality in an AI: A Review3 Feb 2023
- 2023Intrinsic Motivation in Model-based Reinforcement Learning: A Brief Review24 Jan 2023
- 2022Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments18 Nov 2022
- 2022A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement Learning5 Aug 2022
- 2022Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation22 Jun 2022
- 2022BYOL-Explore: Exploration by Bootstrapped Prediction16 Jun 2022
2 more
- 2023Towards A Unified Agent with Foundation Modelscited by 47
- 2023Towards A Unified Agent with Foundation Models18 Jul 2023
- 2022BYOL-Explore: Exploration by Bootstrapped Predictioncited by 73
- 2024Data-driven Discovery with Large Generative Modelscited by 34
- 2023Regularity as Intrinsic Reward for Free Playcited by 5
- 2022Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environmentscited by 4
- 2022A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement Learningcited by 2
- 2023The Construction of Reality in an AI: A Reviewcited by 1
- 2022Physical Derivatives: Computing policy gradients by physical forward-propagationcited by 0
- 2024Data-driven Discovery with Large Generative Models21 Feb 2024
- 2023Regularity as Intrinsic Reward for Free Play3 Dec 2023
- 2023The Construction of Reality in an AI: A Review3 Feb 2023
- 2022Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments18 Nov 2022
- 2022A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement Learning5 Aug 2022
- 2022BYOL-Explore: Exploration by Bootstrapped Prediction16 Jun 2022
- 2022Physical Derivatives: Computing policy gradients by physical forward-propagation15 Jan 2022