Reward-Free RL

RL: Reinforcement Learning

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Deep Epistemic Value Functions for Optimistic Exploration
Sep 28, 2026Leander Diaz-Bone, Marco Bagatella, Jonas Hübotter +1Value Function Estimation RL Exploration
ETH Zürich, Switzerland · Max Planck Institute for Intelligent Systems, Germany
Provably Safe Sim-to-Real Transfer
Sep 1, 2026Tingting Ni, Maryam KamgarpourReinforcement Learning Sim-to-Real Transfer
SYCAMORE, EPFL
SP3O: Reinforcement Learning from Segment Preferences without Reward Modeling
Aug 3, 2026Evan Assmus, Qining Zhang, Lei YingReinforcement Learning Robotic RL
University of Michigan, Ann Arbor
Reward-free Pretraining for Reinforcement Learning via Occupancy Coverage Maximization
Jun 19, 2026Marco Pratticò, Pietro Novelli, Massimiliano Pontil +1Reinforcement Learning Exploration-Exploitation Tradeoff
Computational Statistics and Machine Learning - Istituto Italiano di Tecnologia, 16100 Genova, Italy · 2AI Centre, Computer Science Department, University College London, London, UK.
Online Reward-Punishment Learning from Fixed-Channel Perceptual Event Streams without Environment Rewards
Jun 17, 2026Zirong LiReward Modeling Reinforcement Learning
Tiangong University
Label-Free Reinforcement Learning via Cross-Model Entropy
May 27, 2026Matt Gorbett, Hossein ShiraziReinforcement Learning Instruction Following
Independent Researcher · San Diego State University
A Reward-Free Viewpoint on Multi-Objective Reinforcement Learning
Apr 27, 2026Ying-Tu Chen, Wei Hung, Bing-Shu Wu +2Multi-Objective Reinforcement Learning Reinforcement Learning
National Yang Ming Chiao Tung University, Hsinchu, Taiwan · Massachusetts Institute of Technology
ELEMENT: Episodic and Lifelong Exploration via Maximum Entropy
Dec 5, 2024Hongming Li, Zhao Yang, Xiaoxuan Liang +2Intrinsic Reward Methods Reinforcement Learning
Zhejiang Lab, Huangzhou, China · Vrije Universiteit Amsterdam, Amsterdam, the Netherlands · Computational NeuroEngineering Laboratory at the University of Florida, Gainesville, USA

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Deep Epistemic Value Functions for Optimistic Exploration

Leander Diaz-Bone, Marco Bagatella, Jonas Hübotter, Andreas Krause

ETH Zürich, Switzerland · Max Planck Institute for Intelligent Systems, Germany

Value Function Estimation RL Exploration Model-Free RL Reward-Free RL

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Principled exploration in reinforcement learning requires an agent to quantify its epistemic uncertainty and act to resolve it. Uncertainty over the value function provides a natural signal for exploration, yet existing deep approximations remain brittle and perform inconsistently. The central challenge is therefore to scale these ideas robustly. We conduct a systematic empirical study of how epistemic uncertainty is represented, propagated, and optimized in deep epistemic value functions, and uncover distinct failure modes along each of these axes. These findings motivate DEVOTE, a model-free reinforcement learning algorithm that controls how uncertainty generalizes beyond observed data, stabilizes its temporal propagation, and preserves adaptation to the resulting non-stationary exploration objective. Across reward-free exploration and challenging continuous-control tasks, DEVOTE reaches novel states more effectively and achieves higher task return than strong model-free and model-based exploration baselines. These results provide evidence that deep epistemic value functions are a promising path toward scalable, principled exploration.

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