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

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Showing 801–825 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
GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual ExplanationsCode1
Neural Laplace Control for Continuous-time Delayed SystemsCode1
Model-Based Uncertainty in Value FunctionsCode1
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
Diverse Policy Optimization for Structured Action SpaceCode1
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
Guiding Pretraining in Reinforcement Learning with Large Language ModelsCode1
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement LearningCode1
Procedural generation of meta-reinforcement learning tasksCode1
The Wisdom of Hindsight Makes Language Models Better Instruction FollowersCode1
A SWAT-based Reinforcement Learning Framework for Crop ManagementCode1
On Penalty-based Bilevel Gradient Descent MethodCode1
ManiSkill2: A Unified Benchmark for Generalizable Manipulation SkillsCode1
Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban EnvironmentsCode1
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Benchmark Results

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