SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 801–850 of 15113 papers

TitleStatusHype
Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking OraclesCode1
Neural Airport Ground HandlingCode1
CoRL: Environment Creation and Management Focused on System IntegrationCode1
Preference Transformer: Modeling Human Preferences using Transformers for RLCode1
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement LearningCode1
The In-Sample Softmax for Offline Reinforcement LearningCode1
Model-Based Uncertainty in Value FunctionsCode1
GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual ExplanationsCode1
Neural Laplace Control for Continuous-time Delayed SystemsCode1
Diverse Policy Optimization for Structured Action SpaceCode1
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMsCode1
Energy Harvesting Reconfigurable Intelligent Surface for UAV Based on Robust Deep Reinforcement LearningCode1
Behavior Proximal Policy OptimizationCode1
Deep Reinforcement Learning for Cost-Effective Medical DiagnosisCode1
Swapped goal-conditioned offline reinforcement learningCode1
Dual RL: Unification and New Methods for Reinforcement and Imitation LearningCode1
Semiconductor Fab Scheduling with Self-Supervised and Reinforcement LearningCode1
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement LearningCode1
Guiding Pretraining in Reinforcement Learning with Large Language ModelsCode1
Procedural generation of meta-reinforcement learning tasksCode1
A SWAT-based Reinforcement Learning Framework for Crop ManagementCode1
The Wisdom of Hindsight Makes Language Models Better Instruction FollowersCode1
On Penalty-based Bilevel Gradient Descent MethodCode1
Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban EnvironmentsCode1
ManiSkill2: A Unified Benchmark for Generalizable Manipulation SkillsCode1
RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network ProtocolsCode1
Predictable MDP Abstraction for Unsupervised Model-Based RLCode1
Multi-Task Recommendations with Reinforcement LearningCode1
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
Two-Stage Constrained Actor-Critic for Short Video RecommendationCode1
Learning to Optimize for Reinforcement LearningCode1
Mind the Gap: Offline Policy Optimization for Imperfect RewardsCode1
Policy Expansion for Bridging Offline-to-Online Reinforcement LearningCode1
Internally Rewarded Reinforcement LearningCode1
Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness GuaranteesCode1
Optimizing DDPM Sampling with Shortcut Fine-TuningCode1
Retrosynthetic Planning with Dual Value NetworksCode1
Execution-based Code Generation using Deep Reinforcement LearningCode1
Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic EnvironmentsCode1
Guiding Online Reinforcement Learning with Action-Free Offline PretrainingCode1
Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World ModellingCode1
Outcome-directed Reinforcement Learning by Uncertainty & Temporal Distance-Aware Curriculum Goal GenerationCode1
Deep Laplacian-based Options for Temporally-Extended ExplorationCode1
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple ConstraintsCode1
Distributed Control of Partial Differential Equations Using Convolutional Reinforcement LearningCode1
Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement LearningCode1
PIRLNav: Pretraining with Imitation and RL Finetuning for ObjectNavCode1
A reinforcement learning path planning approach for range-only underwater target localization with autonomous vehiclesCode1
Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as ObservationCode1
schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling ExperimentsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified