mlsanityData to 14 Oct 2026

Learning To Explore With Predictive World Model Via Self-Supervised Learning

arXiv 2502.13200 ↗First posted 18 Feb 2025cs.LG cs.AIcited by 0

On the map: World models → Latent dynamics models and model-based RL

Abstract (arXiv)

Autonomous artificial agents must be able to learn behaviors in complex environments without humans to design tasks and rewards. Designing these functions for each environment is not feasible, thus, motivating the development of intrinsic reward functions. In this paper, we propose using several cognitive elements that have been neglected for a long time to build an internal world model for an intrinsically motivated agent. Our agent performs satisfactory iterations with the environment, learning complex behaviors without needing previously designed reward functions. We used 18 Atari games to evaluate what cognitive skills emerge in games that require reactive and deliberative behaviors. Our results show superior performance compared to the state-of-the-art in many test cases with dense and sparse rewards.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Latent dynamics models and model-based RL, one of 11 directions in World models. Builds on 6 of its 27 references that are in the map. First posted on arXiv 18 Feb 2025.

Builds on

6 of 27 references.

References · 6

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Large-Scale Study of Curiosity-Driven Learningcited by 280
  3. 2019Recurrent Independent Mechanismscited by 219
  4. 2015Incentivizing Exploration In Reinforcement Learning With Deep Predictive Modelscited by 154
  5. 2017Surprise-Based Intrinsic Motivation for Deep Reinforcement Learningcited by 83
  6. 2019A survey on intrinsic motivation in reinforcement learningcited by 81

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2018Learning to Play with Intrinsically-Motivated Self-Aware Agents
  2. 2023Intrinsic Motivation in Model-based Reinforcement Learning: A Brief Review
  3. 2025Autotelic Reinforcement Learning: Exploring Intrinsic Motivations for Skill Acquisition in Open-Ended Environments
  4. 2024The intrinsic motivation of reinforcement and imitation learning for sequential tasks
  5. 2022Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation
  6. 2026Player-1: Learning to Play from Synthetic Experience
  7. 2023Motif: Intrinsic Motivation from Artificial Intelligence Feedback
  8. 2020Mastering Atari with Discrete World Models