{"total":106,"rows":[{"id":"2110.11328","title":"A Fine-Grained Analysis on Distribution Shift","created":"2021-10-21","cited_by_count":171,"area":null},{"id":"2111.09794","title":"A Survey of Zero-shot Generalisation in Deep Reinforcement Learning","created":"2021-11-18","cited_by_count":170,"area":null},{"id":"2206.01101","title":"Weakly Supervised Representation Learning with Sparse Perturbations","created":"2022-06-02","cited_by_count":65,"area":null},{"id":"2106.15612","title":"Learning Task Informed Abstractions","created":"2021-06-29","cited_by_count":58,"area":null},{"id":"2107.00646","title":"Learning to See before Learning to Act: Visual Pre-training for Manipulation","created":"2021-07-01","cited_by_count":51,"area":null},{"id":"2210.07241","title":"Visual Reinforcement Learning with Self-Supervised 3D Representations","created":"2022-10-13","cited_by_count":45,"area":null},{"id":"2106.04379","title":"Learning Markov State Abstractions for Deep Reinforcement Learning","created":"2021-06-08","cited_by_count":44,"area":null},{"id":"2202.03390","title":"Geometric Multimodal Contrastive Representation Learning","created":"2022-02-07","cited_by_count":40,"area":null},{"id":"2102.05714","title":"Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation","created":"2021-02-10","cited_by_count":35,"area":null},{"id":"2209.09203","title":"Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning","created":"2022-09-16","cited_by_count":34,"area":null},{"id":"2106.08053","title":"On the Power of Multitask Representation Learning in Linear MDP","created":"2021-06-15","cited_by_count":30,"area":null},{"id":"2210.01768","title":"Disentanglement with Biological Constraints: A Theory of Functional Cell Types","created":"2022-09-30","cited_by_count":30,"area":null},{"id":"2305.14133","title":"Conditional Mutual Information for Disentangled Representations in Reinforcement Learning","created":"2023-05-23","cited_by_count":27,"area":null},{"id":"2111.03189","title":"Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning","created":"2021-11-04","cited_by_count":26,"area":null},{"id":"2203.09250","title":"Symmetry-Based Representations for Artificial and Biological General Intelligence","created":"2022-03-17","cited_by_count":26,"area":null},{"id":"2203.15955","title":"Investigating the Properties of Neural Network Representations in Reinforcement Learning","created":"2022-03-30","cited_by_count":24,"area":null},{"id":"2307.01452","title":"Causal Reinforcement Learning: A Survey","created":"2023-07-04","cited_by_count":23,"area":null},{"id":"2106.02193","title":"Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL","created":"2021-06-04","cited_by_count":20,"area":null},{"id":"2103.07607","title":"Solving Compositional Reinforcement Learning Problems via Task Reduction","created":"2021-03-13","cited_by_count":19,"area":null},{"id":"2202.05839","title":"Abstraction for Deep Reinforcement Learning","created":"2022-02-10","cited_by_count":19,"area":null},{"id":"2207.05480","title":"Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning","created":"2022-07-12","cited_by_count":19,"area":null},{"id":"2201.00248","title":"Transfer RL across Observation Feature Spaces via Model-Based Regularization","created":"2022-01-01","cited_by_count":18,"area":null},{"id":"2208.14226","title":"Unsupervised Representation Learning in Deep Reinforcement Learning: A Review","created":"2022-08-27","cited_by_count":18,"area":null},{"id":"2104.05043","title":"Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning","created":"2021-04-11","cited_by_count":17,"area":null},{"id":"2103.08255","title":"Sample-efficient Reinforcement Learning Representation Learning with Curiosity Contrastive Forward Dynamics Model","created":"2021-03-15","cited_by_count":16,"area":null},{"id":"2203.15664","title":"Nearly Minimax Algorithms for Linear Bandits with Shared Representation","created":"2022-03-29","cited_by_count":16,"area":null},{"id":"2110.12997","title":"Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning","created":"2021-10-25","cited_by_count":15,"area":null},{"id":"2102.07559","title":"Certifiably Robust Variational Autoencoders","created":"2021-02-15","cited_by_count":14,"area":null},{"id":"2203.12117","title":"NovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty","created":"2022-03-23","cited_by_count":14,"area":null},{"id":"2204.04905","title":"Evaluating Vision Transformer Methods for Deep Reinforcement Learning from Pixels","created":"2022-04-11","cited_by_count":13,"area":null},{"id":"2103.02886","title":"Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings","created":"2021-03-04","cited_by_count":12,"area":null},{"id":"2107.05686","title":"The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents","created":"2021-07-12","cited_by_count":12,"area":null},{"id":"2310.15940","title":"Combining Behaviors with the Successor Features Keyboard","created":"2023-10-24","cited_by_count":12,"area":null},{"id":"2204.02283","title":"Lost in Latent Space: Disentangled Models and the Challenge of Combinatorial Generalisation","created":"2022-04-05","cited_by_count":11,"area":null},{"id":"2209.08660","title":"Learn the Time to Learn: Replay Scheduling in Continual Learning","created":"2022-09-18","cited_by_count":11,"area":null},{"id":"2302.01928","title":"Aligning Robot and Human Representations","created":"2023-02-03","cited_by_count":11,"area":null},{"id":"2501.13075","title":"Evolution and The Knightian Blindspot of Machine Learning","created":"2025-01-22","cited_by_count":11,"area":null},{"id":"2108.05779","title":"DiagViB-6: A Diagnostic Benchmark Suite for Vision Models in the Presence of Shortcut and Generalization Opportunities","created":"2021-08-12","cited_by_count":10,"area":null},{"id":"2203.09940","title":"Alleviating Adversarial Attacks on Variational Autoencoders with MCMC","created":"2022-03-18","cited_by_count":10,"area":null},{"id":"2306.02419","title":"Bad Habits: Policy Confounding and Out-of-Trajectory Generalization in RL","created":"2023-06-04","cited_by_count":10,"area":null},{"id":"2204.06355","title":"Local Feature Swapping for Generalization in Reinforcement Learning","created":"2022-04-13","cited_by_count":9,"area":null},{"id":"2112.14754","title":"Disentanglement and Generalization Under Correlation Shifts","created":"2021-12-29","cited_by_count":8,"area":null},{"id":"2303.01384","title":"DAVA: Disentangling Adversarial Variational Autoencoder","created":"2023-03-02","cited_by_count":8,"area":null},{"id":"2303.05069","title":"Conceptual Reinforcement Learning for Language-Conditioned Tasks","created":"2023-03-09","cited_by_count":8,"area":null},{"id":"2307.10524","title":"Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions","created":"2023-07-20","cited_by_count":8,"area":null},{"id":"2105.14780","title":"Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning","created":"2021-05-31","cited_by_count":7,"area":null},{"id":"2209.07007","title":"Gromov-Wasserstein Autoencoders","created":"2022-09-15","cited_by_count":7,"area":null},{"id":"2307.11608","title":"Learning minimal representations of stochastic processes with variational autoencoders","created":"2023-07-21","cited_by_count":7,"area":null},{"id":"2401.03599","title":"Disentangled Neural Relational Inference for Interpretable Motion Prediction","created":"2024-01-07","cited_by_count":7,"area":null},{"id":"2110.03608","title":"How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents","created":"2021-10-07","cited_by_count":6,"area":null},{"id":"2205.08716","title":"No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL","created":"2022-05-18","cited_by_count":6,"area":null},{"id":"2312.09436","title":"Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy","created":"2023-11-27","cited_by_count":6,"area":null},{"id":"2208.00659","title":"Model-based graph reinforcement learning for inductive traffic signal control","created":"2022-08-01","cited_by_count":5,"area":null},{"id":"2304.14511","title":"Visual Referential Games Further the Emergence of Disentangled Representations","created":"2023-04-27","cited_by_count":5,"area":null},{"id":"2106.08902","title":"Adaptive Clustering and Personalization in Multi-Agent Stochastic Linear Bandits","created":"2021-06-15","cited_by_count":4,"area":null},{"id":"2110.03318","title":"On the Latent Holes of VAEs for Text Generation","created":"2021-10-07","cited_by_count":4,"area":null},{"id":"2207.07749","title":"Bootstrap State Representation using Style Transfer for Better Generalization in Deep Reinforcement Learning","created":"2022-07-15","cited_by_count":4,"area":null},{"id":"2209.12347","title":"Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation","created":"2022-09-25","cited_by_count":4,"area":null},{"id":"2212.13088","title":"Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities","created":"2022-12-26","cited_by_count":4,"area":null},{"id":"2303.17508","title":"Learning in Factored Domains with Information-Constrained Visual Representations","created":"2023-03-30","cited_by_count":4,"area":null},{"id":"2310.19137","title":"Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning","created":"2023-10-29","cited_by_count":4,"area":null},{"id":"2102.08581","title":"Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning","created":"2021-02-17","cited_by_count":3,"area":null},{"id":"2203.08454","title":"Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents","created":"2022-03-16","cited_by_count":3,"area":null},{"id":"2306.00202","title":"Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation","created":"2023-05-31","cited_by_count":3,"area":null},{"id":"2309.12628","title":"Sequential Action-Induced Invariant Representation for Reinforcement Learning","created":"2023-09-22","cited_by_count":3,"area":null},{"id":"2502.02377","title":"A Minimax Approach to Ad Hoc Teamwork","created":"2025-02-04","cited_by_count":3,"area":null},{"id":"2105.06660","title":"Estimating Disentangled Belief about Hidden State and Hidden Task for Meta-RL","created":"2021-05-14","cited_by_count":2,"area":null},{"id":"2106.05139","title":"Pretrained Encoders are All You Need","created":"2021-06-09","cited_by_count":2,"area":null},{"id":"2107.06405","title":"Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks","created":"2021-07-13","cited_by_count":2,"area":null},{"id":"2109.13588","title":"Making Curiosity Explicit in Vision-based RL","created":"2021-09-28","cited_by_count":2,"area":null},{"id":"2110.00784","title":"Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning","created":"2021-10-02","cited_by_count":2,"area":null},{"id":"2202.00391","title":"Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement","created":"2022-02-01","cited_by_count":2,"area":null},{"id":"2208.04149","title":"Computer Vision Methods for the Microstructural Analysis of Materials: The State-of-the-art and Future Perspectives","created":"2022-07-29","cited_by_count":2,"area":null},{"id":"2208.14863","title":"Style-Agnostic Reinforcement Learning","created":"2022-08-31","cited_by_count":2,"area":null},{"id":"2210.09646","title":"RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning","created":"2022-10-18","cited_by_count":2,"area":null},{"id":"2210.14524","title":"A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications","created":"2022-10-26","cited_by_count":2,"area":null},{"id":"2211.00086","title":"Disentangled (Un)Controllable Features","created":"2022-10-31","cited_by_count":2,"area":null},{"id":"2303.02234","title":"Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning","created":"2023-03-03","cited_by_count":2,"area":null},{"id":"2308.15550","title":"Adversarial Style Transfer for Robust Policy Optimization in Deep Reinforcement Learning","created":"2023-08-29","cited_by_count":2,"area":null},{"id":"2405.06063","title":"A Minimalist Prompt for Zero-Shot Policy Learning","created":"2024-05-09","cited_by_count":2,"area":null},{"id":"2102.03986","title":"DEFT: Distilling Entangled Factors by Preventing Information Diffusion","created":"2021-02-08","cited_by_count":1,"area":null},{"id":"2105.08568","title":"Fixed $\\beta$-VAE Encoding for Curious Exploration in Complex 3D Environments","created":"2021-05-18","cited_by_count":1,"area":null},{"id":"2106.07995","title":"Learning of feature points without additional supervision improves reinforcement learning from images","created":"2021-06-15","cited_by_count":1,"area":null},{"id":"2110.12185","title":"Group-disentangled Representation Learning with Weakly-Supervised Regularization","created":"2021-10-23","cited_by_count":1,"area":null},{"id":"2111.08733","title":"Learning Provably Robust Motion Planners Using Funnel Libraries","created":"2021-11-16","cited_by_count":1,"area":null},{"id":"2202.08444","title":"A Survey on Deep Reinforcement Learning-based Approaches for Adaptation and Generalization","created":"2022-02-17","cited_by_count":1,"area":null},{"id":"2303.07925","title":"Deep incremental learning models for financial temporal tabular datasets with distribution shifts","created":"2023-03-14","cited_by_count":1,"area":null},{"id":"2306.05727","title":"The Role of Diverse Replay for Generalisation in Reinforcement Learning","created":"2023-06-09","cited_by_count":1,"area":null},{"id":"2403.18270","title":"Image Deraining via Self-supervised Reinforcement Learning","created":"2024-03-27","cited_by_count":1,"area":null},{"id":"2409.00269","title":"Leveraging a Cognitive Model to Measure Subjective Similarity of Human and GPT-4 Written Content","created":"2024-08-30","cited_by_count":1,"area":null},{"id":"2510.20165","title":"IB-GAN: Disentangled Representation Learning with Information Bottleneck Generative Adversarial Networks","created":"2025-10-23","cited_by_count":1,"area":null},{"id":"2102.12017","title":"Annotating Motion Primitives for Simplifying Action Search in Reinforcement Learning","created":"2021-02-24","cited_by_count":0,"area":null},{"id":"2107.03690","title":"Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)","created":"2021-07-08","cited_by_count":0,"area":null},{"id":"2112.08369","title":"Feature-Attending Recurrent Modules for Generalization in Reinforcement Learning","created":"2021-12-15","cited_by_count":0,"area":null},{"id":"2202.02395","title":"Malleable Agents for Re-Configurable Robotic Manipulators","created":"2022-02-04","cited_by_count":0,"area":null},{"id":"2206.00518","title":"Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning","created":"2022-06-01","cited_by_count":0,"area":null},{"id":"2301.13343","title":"Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement Learning","created":"2023-01-31","cited_by_count":0,"area":null},{"id":"2302.06009","title":"Policy-Induced Self-Supervision Improves Representation Finetuning in Visual RL","created":"2023-02-12","cited_by_count":0,"area":null},{"id":"2303.06695","title":"The tree reconstruction game: phylogenetic reconstruction using reinforcement learning","created":"2023-03-12","cited_by_count":0,"area":null},{"id":"2304.14533","title":"Adversarial Policy Optimization in Deep Reinforcement Learning","created":"2023-04-27","cited_by_count":0,"area":null}],"next_cursor":"100"}