{"total":72,"rows":[{"id":"2103.02503","title":"Domain Generalization: A Survey","created":"2021-03-03","cited_by_count":764,"area":null},{"id":"2104.02008","title":"Domain Generalization with MixStyle","created":"2021-04-05","cited_by_count":566,"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":"2102.03065","title":"Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity","created":"2021-02-05","cited_by_count":128,"area":null},{"id":"2101.05265","title":"Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning","created":"2021-01-13","cited_by_count":123,"area":null},{"id":"2110.05038","title":"Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs","created":"2021-10-11","cited_by_count":122,"area":null},{"id":"2107.06277","title":"Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability","created":"2021-07-13","cited_by_count":109,"area":null},{"id":"2102.09430","title":"State Entropy Maximization with Random Encoders for Efficient Exploration","created":"2021-02-18","cited_by_count":108,"area":null},{"id":"2103.16559","title":"Broaden Your Views for Self-Supervised Video Learning","created":"2021-03-30","cited_by_count":108,"area":null},{"id":"2102.10330","title":"Decoupling Value and Policy for Generalization in Reinforcement Learning","created":"2021-02-20","cited_by_count":82,"area":null},{"id":"2106.07876","title":"Vision-Language Navigation with Random Environmental Mixup","created":"2021-06-15","cited_by_count":70,"area":null},{"id":"2107.02053","title":"MixStyle Neural Networks for Domain Generalization and Adaptation","created":"2021-07-05","cited_by_count":64,"area":null},{"id":"2104.14385","title":"Cross-Domain Few-Shot Classification via Adversarial Task Augmentation","created":"2021-04-29","cited_by_count":63,"area":null},{"id":"2212.08860","title":"Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning","created":"2022-12-17","cited_by_count":60,"area":null},{"id":"2106.04531","title":"RobustNav: Towards Benchmarking Robustness in Embodied Navigation","created":"2021-06-08","cited_by_count":54,"area":null},{"id":"2102.04376","title":"Adversarially Guided Actor-Critic","created":"2021-02-08","cited_by_count":47,"area":null},{"id":"2106.09678","title":"SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies","created":"2021-06-17","cited_by_count":43,"area":null},{"id":"2106.10268","title":"MADE: Exploration via Maximizing Deviation from Explored Regions","created":"2021-06-18","cited_by_count":40,"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":"2306.05483","title":"On the Importance of Exploration for Generalization in Reinforcement Learning","created":"2023-06-08","cited_by_count":31,"area":null},{"id":"2209.07364","title":"Continuous MDP Homomorphisms and Homomorphic Policy Gradient","created":"2022-09-15","cited_by_count":27,"area":null},{"id":"2106.04823","title":"Taxonomy of Machine Learning Safety: A Survey and Primer","created":"2021-06-09","cited_by_count":25,"area":null},{"id":"2210.04561","title":"A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning","created":"2022-10-10","cited_by_count":25,"area":null},{"id":"2306.16612","title":"GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps","created":"2023-06-29","cited_by_count":23,"area":null},{"id":"2307.01452","title":"Causal Reinforcement Learning: A Survey","created":"2023-07-04","cited_by_count":23,"area":null},{"id":"2205.15704","title":"Mitigating Dataset Bias by Using Per-sample Gradient","created":"2022-05-31","cited_by_count":22,"area":null},{"id":"2306.03072","title":"Explore to Generalize in Zero-Shot RL","created":"2023-06-05","cited_by_count":20,"area":null},{"id":"2104.14223","title":"InsertionNet -- A Scalable Solution for Insertion","created":"2021-04-29","cited_by_count":16,"area":null},{"id":"2307.00972","title":"MoVie: Visual Model-Based Policy Adaptation for View Generalization","created":"2023-07-03","cited_by_count":16,"area":null},{"id":"2106.15587","title":"Generalization of Reinforcement Learning with Policy-Aware Adversarial Data Augmentation","created":"2021-06-29","cited_by_count":15,"area":null},{"id":"2205.10218","title":"Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions","created":"2022-05-20","cited_by_count":15,"area":null},{"id":"2210.14985","title":"Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing","created":"2022-10-26","cited_by_count":15,"area":null},{"id":"2302.12003","title":"Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions","created":"2023-02-12","cited_by_count":14,"area":null},{"id":"2105.13524","title":"Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics Mixture","created":"2021-05-28","cited_by_count":12,"area":null},{"id":"2405.18917","title":"Causal Action Influence Aware Counterfactual Data Augmentation","created":"2024-05-29","cited_by_count":12,"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":"2305.05666","title":"Policy Gradient Methods in the Presence of Symmetries and State Abstractions","created":"2023-05-09","cited_by_count":9,"area":null},{"id":"2406.16422","title":"Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting","created":"2024-06-24","cited_by_count":9,"area":null},{"id":"2502.20162","title":"Gradient-Guided Annealing for Domain Generalization","created":"2025-02-27","cited_by_count":9,"area":null},{"id":"2106.10365","title":"Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments","created":"2021-06-18","cited_by_count":6,"area":null},{"id":"2205.00943","title":"CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning","created":"2022-05-02","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":"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":"2302.09601","title":"Generalization in Visual Reinforcement Learning with the Reward Sequence Distribution","created":"2023-02-19","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":"2210.13378","title":"ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement Learning","created":"2022-10-24","cited_by_count":3,"area":null},{"id":"2301.12776","title":"PAC-Bayesian Soft Actor-Critic Learning","created":"2023-01-30","cited_by_count":3,"area":null},{"id":"2305.04180","title":"Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity","created":"2023-05-07","cited_by_count":3,"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":"2211.06733","title":"Deep Reinforcement Learning with Vector Quantized Encoding","created":"2022-11-12","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":"2107.03015","title":"Evaluating the progress of Deep Reinforcement Learning in the real world: aligning domain-agnostic and domain-specific research","created":"2021-07-07","cited_by_count":1,"area":null},{"id":"2109.08236","title":"Reinforcement Learning on Encrypted Data","created":"2021-09-16","cited_by_count":1,"area":null},{"id":"2112.05218","title":"Learning Generalizable Behavior via Visual Rewrite Rules","created":"2021-12-09","cited_by_count":1,"area":null},{"id":"2210.02224","title":"Neural Distillation as a State Representation Bottleneck in Reinforcement Learning","created":"2022-10-05","cited_by_count":1,"area":null},{"id":"2212.01189","title":"Denoising after Entropy-based Debiasing A Robust Training Method for Dataset Bias with Noisy Labels","created":"2022-12-01","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":"2306.08537","title":"VIBR: Learning View-Invariant Value Functions for Robust Visual Control","created":"2023-06-14","cited_by_count":1,"area":null},{"id":"2401.09258","title":"Efficient Training of Generalizable Visuomotor Policies via Control-Aware Augmentation","created":"2024-01-17","cited_by_count":1,"area":null},{"id":"2410.03565","title":"Training on Irrelevant States Implies Data Augmentation: Generalization in Contextual MDPs","created":"2024-10-04","cited_by_count":1,"area":null},{"id":"2108.02323","title":"Active Reinforcement Learning over MDPs","created":"2021-08-05","cited_by_count":0,"area":null},{"id":"2112.04084","title":"Hyper-parameter optimization based on soft actor critic and hierarchical mixture regularization","created":"2021-12-08","cited_by_count":0,"area":null},{"id":"2112.06055","title":"Towards Autonomous Satellite Communications: An AI-based Framework to Address System-level Challenges","created":"2021-12-11","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":"2204.14076","title":"Dynamic Noises of Multi-Agent Environments Can Improve Generalization: Agent-based Models meets Reinforcement Learning","created":"2022-03-26","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":"2304.14533","title":"Adversarial Policy Optimization in Deep Reinforcement Learning","created":"2023-04-27","cited_by_count":0,"area":null},{"id":"2305.02882","title":"Simple Noisy Environment Augmentation for Reinforcement Learning","created":"2023-05-04","cited_by_count":0,"area":null},{"id":"2503.21640","title":"Towards Fully Automated Decision-Making Systems for Greenhouse Control: Challenges and Opportunities","created":"2025-03-27","cited_by_count":0,"area":null},{"id":"2505.11100","title":"Bidirectional Distillation: A Mixed-Play Framework for Multi-Agent Generalizable Behaviors","created":"2025-05-16","cited_by_count":0,"area":null},{"id":"2605.19649","title":"CAD-Free Learning of Spacecraft Pose Estimators via NeRF-Based Augmentations","created":"2026-05-19","cited_by_count":0,"area":null}],"next_cursor":null}