{"total":159,"rows":[{"id":"2101.11517","title":"Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond","created":"2021-01-27","cited_by_count":153,"area":null},{"id":"2301.07608","title":"Human-Timescale Adaptation in an Open-Ended Task Space","created":"2023-01-18","cited_by_count":138,"area":null},{"id":"2108.11482","title":"ETA Prediction with Graph Neural Networks in Google Maps","created":"2021-08-25","cited_by_count":130,"area":null},{"id":"2111.03026","title":"B-Pref: Benchmarking Preference-Based Reinforcement Learning","created":"2021-11-04","cited_by_count":106,"area":null},{"id":"2212.04458","title":"General-Purpose In-Context Learning by Meta-Learning Transformers","created":"2022-12-08","cited_by_count":105,"area":null},{"id":"2210.05639","title":"Discovered Policy Optimisation","created":"2022-10-11","cited_by_count":104,"area":null},{"id":"2310.00166","title":"Motif: Intrinsic Motivation from Artificial Intelligence Feedback","created":"2023-09-29","cited_by_count":102,"area":null},{"id":"2201.03916","title":"Automated Reinforcement Learning (AutoRL): A Survey and Open Problems","created":"2022-01-11","cited_by_count":70,"area":null},{"id":"2306.01324","title":"Hyperparameters in Reinforcement Learning and How To Tune Them","created":"2023-06-02","cited_by_count":59,"area":null},{"id":"2104.06159","title":"Muesli: Combining Improvements in Policy Optimization","created":"2021-04-13","cited_by_count":53,"area":null},{"id":"2107.03974","title":"Offline Meta-Reinforcement Learning with Online Self-Supervision","created":"2021-07-08","cited_by_count":53,"area":"offlinerl"},{"id":"2110.03909","title":"Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning","created":"2021-10-08","cited_by_count":49,"area":null},{"id":"2109.04504","title":"Bootstrapped Meta-Learning","created":"2021-09-09","cited_by_count":45,"area":null},{"id":"2211.11260","title":"Discovering Evolution Strategies via Meta-Black-Box Optimization","created":"2022-11-21","cited_by_count":44,"area":null},{"id":"2102.07121","title":"Multi-Objective Meta Learning","created":"2021-02-14","cited_by_count":39,"area":null},{"id":"2102.02926","title":"Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents","created":"2021-02-04","cited_by_count":36,"area":null},{"id":"2105.08710","title":"Fast and Slow Learning of Recurrent Independent Mechanisms","created":"2021-05-18","cited_by_count":36,"area":null},{"id":"2111.13485","title":"Learning Long-Term Reward Redistribution via Randomized Return Decomposition","created":"2021-11-26","cited_by_count":33,"area":null},{"id":"2304.03995","title":"Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization","created":"2023-04-08","cited_by_count":33,"area":null},{"id":"2102.06741","title":"Discovery of Options via Meta-Learned Subgoals","created":"2021-02-12","cited_by_count":27,"area":null},{"id":"2109.10781","title":"Introducing Symmetries to Black Box Meta Reinforcement Learning","created":"2021-09-22","cited_by_count":25,"area":null},{"id":"2105.05347","title":"Return-based Scaling: Yet Another Normalisation Trick for Deep RL","created":"2021-05-11","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":"2106.00669","title":"Discovering Diverse Nearly Optimal Policies with Successor Features","created":"2021-06-01","cited_by_count":20,"area":null},{"id":"2202.07940","title":"Meta Knowledge Distillation","created":"2022-02-16","cited_by_count":20,"area":"distill"},{"id":"2207.09405","title":"Bayesian Generational Population-Based Training","created":"2022-07-19","cited_by_count":20,"area":null},{"id":"2302.01275","title":"ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs","created":"2023-02-02","cited_by_count":20,"area":null},{"id":"2103.11883","title":"Regularized Softmax Deep Multi-Agent $Q$-Learning","created":"2021-03-22","cited_by_count":19,"area":null},{"id":"2206.03271","title":"On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning","created":"2022-06-07","cited_by_count":19,"area":null},{"id":"2211.15457","title":"Hypernetworks for Zero-shot Transfer in Reinforcement Learning","created":"2022-11-28","cited_by_count":19,"area":null},{"id":"2306.14052","title":"A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware","created":"2023-06-24","cited_by_count":18,"area":null},{"id":"2311.03695","title":"Context Shift Reduction for Offline Meta-Reinforcement Learning","created":"2023-11-07","cited_by_count":18,"area":"offlinerl"},{"id":"2402.05828","title":"Discovering Temporally-Aware Reinforcement Learning Algorithms","created":"2024-02-08","cited_by_count":18,"area":"deeprl"},{"id":"2211.06934","title":"TorchOpt: An Efficient Library for Differentiable Optimization","created":"2022-11-13","cited_by_count":17,"area":null},{"id":"2410.11448","title":"Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement","created":"2024-10-15","cited_by_count":17,"area":"wm"},{"id":"2206.03383","title":"On the Role of Discount Factor in Offline Reinforcement Learning","created":"2022-06-07","cited_by_count":16,"area":"offlinerl"},{"id":"2310.00239","title":"AdaptNet: Policy Adaptation for Physics-Based Character Control","created":"2023-09-30","cited_by_count":16,"area":null},{"id":"2310.02782","title":"Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design","created":"2023-10-04","cited_by_count":16,"area":null},{"id":"2310.19007","title":"Behavior Alignment via Reward Function Optimization","created":"2023-10-29","cited_by_count":16,"area":null},{"id":"2402.00854","title":"SymbolicAI: A framework for logic-based approaches combining generative models and solvers","created":"2024-02-01","cited_by_count":16,"area":null},{"id":"2403.06826","title":"In-context Exploration-Exploitation for Reinforcement Learning","created":"2024-03-11","cited_by_count":16,"area":"offlinerl"},{"id":"2101.07367","title":"Training Learned Optimizers with Randomly Initialized Learned Optimizers","created":"2021-01-14","cited_by_count":13,"area":null},{"id":"2112.09245","title":"Automated Deep Learning: Neural Architecture Search Is Not the End","created":"2021-12-16","cited_by_count":13,"area":null},{"id":"2505.17895","title":"DataRater: Meta-Learned Dataset Curation","created":"2025-05-23","cited_by_count":13,"area":null},{"id":"2112.15400","title":"A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning","created":"2021-12-31","cited_by_count":12,"area":null},{"id":"2203.01443","title":"Continuous-Time Meta-Learning with Forward Mode Differentiation","created":"2022-03-02","cited_by_count":12,"area":null},{"id":"2209.06159","title":"Meta-Gradients in Non-Stationary Environments","created":"2022-09-13","cited_by_count":11,"area":null},{"id":"2310.07433","title":"Imitation Learning from Observation with Automatic Discount Scheduling","created":"2023-10-11","cited_by_count":10,"area":"offlinerl"},{"id":"2201.05000","title":"Automated Reinforcement Learning: An Overview","created":"2022-01-13","cited_by_count":9,"area":null},{"id":"2102.04881","title":"Measuring Progress in Deep Reinforcement Learning Sample Efficiency","created":"2021-02-09","cited_by_count":8,"area":null},{"id":"2102.11544","title":"Identifying Physical Law of Hamiltonian Systems via Meta-Learning","created":"2021-02-23","cited_by_count":8,"area":null},{"id":"2106.15883","title":"Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL","created":"2021-06-30","cited_by_count":8,"area":null},{"id":"2109.06826","title":"Few-shot Quality-Diversity Optimization","created":"2021-09-14","cited_by_count":8,"area":null},{"id":"2112.00901","title":"Hindsight Task Relabelling: Experience Replay for Sparse Reward Meta-RL","created":"2021-12-02","cited_by_count":8,"area":null},{"id":"2206.06872","title":"On Provably Robust Meta-Bayesian Optimization","created":"2022-06-14","cited_by_count":8,"area":null},{"id":"2602.11779","title":"Temperature as a Meta-Policy: Adaptive Temperature in LLM Reinforcement Learning","created":"2026-02-12","cited_by_count":8,"area":null},{"id":"2010.01112","title":"FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization","created":"2020-10-02","cited_by_count":7,"area":"offlinerl"},{"id":"2109.06362","title":"Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods","created":"2021-09-13","cited_by_count":7,"area":null},{"id":"2202.09701","title":"A History of Meta-gradient: Gradient Methods for Meta-learning","created":"2022-02-20","cited_by_count":7,"area":null},{"id":"2309.14078","title":"ODE-based Recurrent Model-free Reinforcement Learning for POMDPs","created":"2023-09-25","cited_by_count":7,"area":null},{"id":"2103.12694","title":"Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks","created":"2021-03-23","cited_by_count":6,"area":"offlinerl"},{"id":"2106.06508","title":"Preferential Temporal Difference Learning","created":"2021-06-11","cited_by_count":6,"area":null},{"id":"2107.01348","title":"Examining average and discounted reward optimality criteria in reinforcement learning","created":"2021-07-03","cited_by_count":6,"area":null},{"id":"2111.12673","title":"Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning","created":"2021-11-24","cited_by_count":6,"area":null},{"id":"2112.00478","title":"On the Practical Consistency of Meta-Reinforcement Learning Algorithms","created":"2021-12-01","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":"2210.09566","title":"Simple Emergent Action Representations from Multi-Task Policy Training","created":"2022-10-18","cited_by_count":6,"area":null},{"id":"2401.17401","title":"Step-size Optimization for Continual Learning","created":"2024-01-30","cited_by_count":6,"area":null},{"id":"2106.04499","title":"Towards Practical Credit Assignment for Deep Reinforcement Learning","created":"2021-06-08","cited_by_count":5,"area":null},{"id":"2106.13125","title":"Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation","created":"2021-06-24","cited_by_count":5,"area":null},{"id":"2109.09889","title":"A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning","created":"2021-09-21","cited_by_count":5,"area":null},{"id":"2110.07510","title":"Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning","created":"2021-10-14","cited_by_count":5,"area":null},{"id":"2111.00206","title":"One Step at a Time: Pros and Cons of Multi-Step Meta-Gradient Reinforcement Learning","created":"2021-10-30","cited_by_count":5,"area":null},{"id":"2112.01853","title":"Episodic Policy Gradient Training","created":"2021-12-03","cited_by_count":5,"area":null},{"id":"2204.12937","title":"Learning to Transfer Role Assignment Across Team Sizes","created":"2022-04-17","cited_by_count":5,"area":null},{"id":"2207.09071","title":"Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks","created":"2022-07-19","cited_by_count":5,"area":null},{"id":"2209.11303","title":"An Investigation of the Bias-Variance Tradeoff in Meta-Gradients","created":"2022-09-22","cited_by_count":5,"area":null},{"id":"2402.02342","title":"MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters","created":"2024-02-04","cited_by_count":5,"area":null},{"id":"2106.06170","title":"Taylor Expansion of Discount Factors","created":"2021-06-11","cited_by_count":4,"area":null},{"id":"2109.09031","title":"Hindsight Foresight Relabeling for Meta-Reinforcement Learning","created":"2021-09-18","cited_by_count":4,"area":null},{"id":"2202.02363","title":"Meta-Reinforcement Learning with Self-Modifying Networks","created":"2022-02-04","cited_by_count":4,"area":null},{"id":"2202.09699","title":"Selective Credit Assignment","created":"2022-02-20","cited_by_count":4,"area":null},{"id":"2209.08429","title":"Constrained Policy Optimization for Controlled Self-Learning in Conversational AI Systems","created":"2022-09-17","cited_by_count":4,"area":null},{"id":"2501.15034","title":"Divergence-Augmented Policy Optimization","created":"2025-01-25","cited_by_count":4,"area":"deeprl"},{"id":"2503.01885","title":"Learning Policy Committees for Effective Personalization in MDPs with Diverse Tasks","created":"2025-02-26","cited_by_count":4,"area":null},{"id":"2505.00793","title":"Scalable Meta-Learning via Mixed-Mode Differentiation","created":"2025-05-01","cited_by_count":4,"area":null},{"id":"2506.04398","title":"Bridging the Performance Gap Between Target-Free and Target-Based Reinforcement Learning","created":"2025-06-04","cited_by_count":4,"area":"deeprl"},{"id":"2602.16928","title":"Discovering Multiagent Learning Algorithms with Large Language Models","created":"2026-02-18","cited_by_count":4,"area":null},{"id":"2104.14644","title":"What is Going on Inside Recurrent Meta Reinforcement Learning Agents?","created":"2021-04-29","cited_by_count":3,"area":null},{"id":"2111.01946","title":"Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach","created":"2021-11-02","cited_by_count":3,"area":null},{"id":"2201.06468","title":"Chaining Value Functions for Off-Policy Learning","created":"2022-01-17","cited_by_count":3,"area":null},{"id":"2302.14604","title":"IQ-Flow: Mechanism Design for Inducing Cooperative Behavior to Self-Interested Agents in Sequential Social Dilemmas","created":"2023-02-28","cited_by_count":3,"area":null},{"id":"2405.16195","title":"Adaptive $Q$-Network: On-the-fly Target Selection for Deep Reinforcement Learning","created":"2024-05-25","cited_by_count":3,"area":"deeprl"},{"id":"2408.07962","title":"Meta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning","created":"2024-08-15","cited_by_count":3,"area":"deeprl"},{"id":"2409.18827","title":"ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning","created":"2024-09-27","cited_by_count":3,"area":"deeprl"},{"id":"2508.04078","title":"RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting","created":"2025-08-06","cited_by_count":3,"area":"deeprl"},{"id":"2102.12957","title":"Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning","created":"2021-02-24","cited_by_count":2,"area":null},{"id":"2103.08241","title":"Reinforcement Learning with Algorithms from Probabilistic Structure Estimation","created":"2021-03-15","cited_by_count":2,"area":null},{"id":"2104.04174","title":"Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning","created":"2021-04-09","cited_by_count":2,"area":null},{"id":"2106.02745","title":"Neural Auto-Curricula","created":"2021-06-04","cited_by_count":2,"area":null}],"next_cursor":"100"}