{"total":73,"rows":[{"id":"2304.12210","title":"A Cookbook of Self-Supervised Learning","created":"2023-04-24","cited_by_count":250,"area":null},{"id":"2210.06718","title":"Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient","created":"2022-10-13","cited_by_count":161,"area":null},{"id":"2305.14343","title":"Video Prediction Models as Rewards for Reinforcement Learning","created":"2023-05-23","cited_by_count":78,"area":null},{"id":"2306.13229","title":"TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning","created":"2023-06-22","cited_by_count":57,"area":null},{"id":"2401.08898","title":"Bridging State and History Representations: Understanding Self-Predictive RL","created":"2024-01-17","cited_by_count":54,"area":"deeprl"},{"id":"2207.08229","title":"Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models","created":"2022-07-17","cited_by_count":45,"area":null},{"id":"2212.03319","title":"Understanding Self-Predictive Learning for Reinforcement Learning","created":"2022-12-06","cited_by_count":44,"area":null},{"id":"2409.12192","title":"DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control","created":"2024-09-18","cited_by_count":44,"area":"offlinerl"},{"id":"2512.24497","title":"What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?","created":"2025-12-30","cited_by_count":43,"area":"wm"},{"id":"2501.16142","title":"Towards General-Purpose Model-Free Reinforcement Learning","created":"2025-01-27","cited_by_count":41,"area":"deeprl"},{"id":"2209.08890","title":"An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey","created":"2022-09-19","cited_by_count":28,"area":null},{"id":"2306.03186","title":"Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning","created":"2023-06-05","cited_by_count":28,"area":null},{"id":"2510.00739","title":"TD-JEPA: Latent-predictive Representations for Zero-Shot Reinforcement Learning","created":"2025-10-01","cited_by_count":22,"area":"deeprl"},{"id":"2312.17227","title":"Gradient-based Planning with World Models","created":"2023-12-28","cited_by_count":19,"area":"wm"},{"id":"2403.04082","title":"Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference","created":"2024-03-06","cited_by_count":17,"area":null},{"id":"2211.00164","title":"Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information","created":"2022-10-31","cited_by_count":16,"area":"offlinerl"},{"id":"2306.15934","title":"Curious Replay for Model-based Adaptation","created":"2023-06-28","cited_by_count":16,"area":null},{"id":"2410.18076","title":"Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration","created":"2024-10-23","cited_by_count":13,"area":null},{"id":"2312.03395","title":"Diffused Task-Agnostic Milestone Planner","created":"2023-12-06","cited_by_count":12,"area":"diffusion"},{"id":"2403.13765","title":"Towards Principled Representation Learning from Videos for Reinforcement Learning","created":"2024-03-20","cited_by_count":10,"area":"deeprl"},{"id":"2405.19548","title":"RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning","created":"2024-05-29","cited_by_count":10,"area":"deeprl"},{"id":"2305.01521","title":"Unlocking the Power of Representations in Long-term Novelty-based Exploration","created":"2023-05-02","cited_by_count":9,"area":null},{"id":"2305.10865","title":"Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning","created":"2023-05-18","cited_by_count":8,"area":"agents"},{"id":"2306.12356","title":"Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP","created":"2023-06-21","cited_by_count":8,"area":null},{"id":"2412.06438","title":"Can foundation models actively gather information in interactive environments to test hypotheses?","created":"2024-12-09","cited_by_count":8,"area":null},{"id":"2304.00046","title":"Accelerating exploration and representation learning with offline pre-training","created":"2023-03-31","cited_by_count":7,"area":null},{"id":"2406.02035","title":"A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning","created":"2024-06-04","cited_by_count":7,"area":"deeprl"},{"id":"2505.03176","title":"seq-JEPA: Autoregressive Predictive Learning of Invariant-Equivariant World Models","created":"2025-05-06","cited_by_count":7,"area":"wm"},{"id":"2305.08932","title":"MIMEx: Intrinsic Rewards from Masked Input Modeling","created":"2023-05-15","cited_by_count":6,"area":null},{"id":"2307.03486","title":"Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning","created":"2023-07-07","cited_by_count":6,"area":null},{"id":"2401.05946","title":"Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments","created":"2024-01-11","cited_by_count":6,"area":"wm"},{"id":"2508.13113","title":"Contrastive Representations for Temporal Reasoning","created":"2025-08-18","cited_by_count":6,"area":"lr"},{"id":"2302.04009","title":"Investigating the role of model-based learning in exploration and transfer","created":"2023-02-08","cited_by_count":5,"area":null},{"id":"2303.11369","title":"Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale","created":"2023-03-20","cited_by_count":5,"area":"offlinerl"},{"id":"2307.00668","title":"Active Sensing with Predictive Coding and Uncertainty Minimization","created":"2023-07-02","cited_by_count":5,"area":null},{"id":"2409.18330","title":"DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors","created":"2024-09-26","cited_by_count":5,"area":"offlinerl"},{"id":"2604.25416","title":"Biased Dreams: Limitations to Epistemic Uncertainty Quantification in Latent Dynamics Models","created":"2026-04-28","cited_by_count":5,"area":"deeprl"},{"id":"2211.10515","title":"Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments","created":"2022-11-18","cited_by_count":4,"area":null},{"id":"2402.03055","title":"Deep Exploration with PAC-Bayes","created":"2024-02-05","cited_by_count":4,"area":"deeprl"},{"id":"2402.07102","title":"An Empirical Study on the Power of Future Prediction in Partially Observable Environments","created":"2024-02-11","cited_by_count":4,"area":"deeprl"},{"id":"2509.10156","title":"LayerLock: Non-collapsing Representation Learning with Progressive Freezing","created":"2025-09-12","cited_by_count":4,"area":null},{"id":"2307.04962","title":"Intrinsically motivated graph exploration using network theories of human curiosity","created":"2023-07-11","cited_by_count":3,"area":null},{"id":"2402.03138","title":"Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representations","created":"2024-02-05","cited_by_count":3,"area":"deeprl"},{"id":"2501.00913","title":"$\\beta$-DQN: Improving Deep Q-Learning By Evolving the Behavior","created":"2025-01-01","cited_by_count":3,"area":"deeprl"},{"id":"2503.11339","title":"Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model","created":"2025-03-14","cited_by_count":3,"area":"offlinerl"},{"id":"2302.11563","title":"Self-supervised network distillation: an effective approach to exploration in sparse reward environments","created":"2023-02-22","cited_by_count":2,"area":null},{"id":"2305.00654","title":"Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition","created":"2023-05-01","cited_by_count":2,"area":null},{"id":"2404.14552","title":"Generalizing Multi-Step Inverse Models for Representation Learning to Finite-Memory POMDPs","created":"2024-04-22","cited_by_count":2,"area":"deeprl"},{"id":"2405.07838","title":"Adaptive Exploration for Data-Efficient General Value Function Evaluations","created":"2024-05-13","cited_by_count":2,"area":"deeprl"},{"id":"2406.10127","title":"Exploration by Learning Diverse Skills through Successor State Measures","created":"2024-06-14","cited_by_count":2,"area":null},{"id":"2505.22772","title":"Calibrated Value-Aware Model Learning with Probabilistic Environment Models","created":"2025-05-28","cited_by_count":2,"area":"deeprl"},{"id":"2603.02008","title":"Temporal Representations for Exploration: Learning Complex Exploratory Behavior without Extrinsic Rewards","created":"2026-03-02","cited_by_count":2,"area":"deeprl"},{"id":"2603.07083","title":"Dreamer-CDP: Improving Reconstruction-free World Models Via Continuous Deterministic Representation Prediction","created":"2026-03-07","cited_by_count":2,"area":"wm"},{"id":"2306.17366","title":"$\\lambda$-models: Effective Decision-Aware Reinforcement Learning with Latent Models","created":"2023-06-30","cited_by_count":1,"area":null},{"id":"2404.00651","title":"Learning Off-policy with Model-based Intrinsic Motivation For Active Online Exploration","created":"2024-03-31","cited_by_count":1,"area":"deeprl"},{"id":"2410.01745","title":"PreND: Enhancing Intrinsic Motivation in Reinforcement Learning through Pre-trained Network Distillation","created":"2024-10-02","cited_by_count":1,"area":"deeprl"},{"id":"2410.11155","title":"Latent-Predictive Empowerment: Measuring Empowerment without a Simulator","created":"2024-10-15","cited_by_count":1,"area":null},{"id":"2505.11044","title":"Exploration by Random Distribution Distillation","created":"2025-05-16","cited_by_count":1,"area":"deeprl"},{"id":"2505.21119","title":"Universal Value-Function Uncertainties","created":"2025-05-27","cited_by_count":1,"area":"offlinerl"},{"id":"2509.05735","title":"Offline vs. Online Learning in Model-based RL: Lessons for Data Collection Strategies","created":"2025-09-06","cited_by_count":1,"area":"wm"},{"id":"2602.02381","title":"Self-Supervised Learning from Structural Invariance","created":"2026-02-02","cited_by_count":1,"area":null},{"id":"2603.22273","title":"Decoupling Exploration and Policy Optimization: Uncertainty Guided Tree Search for Hard Exploration","created":"2026-03-23","cited_by_count":1,"area":"deeprl"},{"id":"2605.11711","title":"Debiased Model-based Representations for Sample-efficient Continuous Control","created":"2026-05-12","cited_by_count":1,"area":"deeprl"},{"id":"2209.05333","title":"Rollout Total Correlation for Deep Reinforcement Learning","created":"2022-09-12","cited_by_count":0,"area":null},{"id":"2211.03521","title":"On the importance of data collection for training general goal-reaching policies","created":"2022-11-07","cited_by_count":0,"area":null},{"id":"2410.04498","title":"AdaMemento: Adaptive Memory-Assisted Policy Optimization for Reinforcement Learning","created":"2024-10-06","cited_by_count":0,"area":"deeprl"},{"id":"2505.08453","title":"Parameter Estimation using Reinforcement Learning Causal Curiosity: Limits and Challenges","created":"2025-05-13","cited_by_count":0,"area":"deeprl"},{"id":"2505.16734","title":"Maximum Total Correlation Reinforcement Learning","created":"2025-05-22","cited_by_count":0,"area":"deeprl"},{"id":"2602.19964","title":"On the Equivalence of Random Network Distillation, Deep Ensembles, and Bayesian Inference","created":"2026-02-23","cited_by_count":0,"area":null},{"id":"2604.15289","title":"Abstract Sim2Real through Approximate Information States","created":"2026-04-16","cited_by_count":0,"area":"deeprl"},{"id":"2606.21271","title":"Reward-free Pretraining for Reinforcement Learning via Occupancy Coverage Maximization","created":"2026-06-19","cited_by_count":0,"area":"deeprl"},{"id":"2608.07870","title":"V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control","created":"2026-08-08","cited_by_count":0,"area":"deeprl"},{"id":"2610.14302","title":"Learning to See through Action: Predictive Curiosity for Robots","created":"2026-10-09","cited_by_count":0,"area":"wm"}],"next_cursor":null}