mlsanityData to 14 Oct 2026

Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration

arXiv 2109.08603 ↗First posted 17 Sep 2021cs.LG cs.NE cs.ROcited by 14

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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

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,194
  3. 2018Exploration by Random Network Distillationcited by 993
  4. 2018Diversity is All You Need: Learning Skills without a Reward Functioncited by 752
  5. 2016Unifying Count-Based Exploration and Intrinsic Motivationcited by 639
  6. 2020Planning to Explore via Self-Supervised World Modelscited by 368
  7. 2018Maximum a Posteriori Policy Optimisationcited by 328
  8. 2019Learning Latent Plans from Playcited by 316
  9. 2019Dynamics-Aware Unsupervised Discovery of Skillscited by 313
  10. 2018Large-Scale Study of Curiosity-Driven Learningcited by 280
  11. 2016Variational Intrinsic Controlcited by 267
  12. 2019Self-Supervised Exploration via Disagreementcited by 265
13 more
  1. 2018Learning by Playing - Solving Sparse Reward Tasks from Scratchcited by 167
  2. 2015Variational Information Maximisation for Intrinsically Motivated Reinforcement Learningcited by 144
  3. 2017Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Playcited by 141
  4. 2018Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Controlcited by 113
  5. 2017Surprise-Based Intrinsic Motivation for Deep Reinforcement Learningcited by 83
  6. 2016Loss is its own Reward: Self-Supervision for Reinforcement Learningcited by 72
  7. 2021Asymmetric self-play for automatic goal discovery in robotic manipulationcited by 72
  8. 2018Learning to Play with Intrinsically-Motivated Self-Aware Agentscited by 35
  9. 2020Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learningcited by 31
  10. 2020Data-efficient Hindsight Off-policy Option Learningcited by 31
  11. 2019Learning Gentle Object Manipulation with Curiosity-Driven Deep Reinforcement Learningcited by 26
  12. 2020Representation Matters: Improving Perception and Exploration for Roboticscited by 11
  13. 2020Simple Sensor Intentions for Explorationcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 6; 2023: 4; 2024: 2; 2025: 1; 2026: 1.

14 papers.

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