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 1701–1750 of 15113 papers

TitleStatusHype
Off-Policy Reinforcement Learning with High Dimensional Reward—0
SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion PlanningCode4
Introduction to Reinforcement Learning—0
Integrating Saliency Ranking and Reinforcement Learning for Enhanced Object DetectionCode1
Value of Information and Reward Specification in Active Inference and POMDPs—0
GFlowNet Training by Policy GradientsCode0
Online Optimization of Curriculum Learning Schedules using Evolutionary Optimization—0
The Bandit Whisperer: Communication Learning for Restless Bandits—0
Optimizing Portfolio with Two-Sided Transactions and Lending: A Reinforcement Learning Framework—0
Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear MDPs—0
Listwise Reward Estimation for Offline Preference-based Reinforcement LearningCode1
Deep Reinforcement Learning for the Design of Metamaterial Mechanisms with Functional Compliance Control—0
Model-Based Transfer Learning for Contextual Reinforcement LearningCode1
Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary ObjectivesCode0
PLANRL: A Motion Planning and Imitation Learning Framework to Bootstrap Reinforcement Learning—0
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes—0
Model-free optimal controller for discrete-time Markovian jump linear systems: A Q-learning approach—0
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
CADRL: Category-aware Dual-agent Reinforcement Learning for Explainable Recommendations over Knowledge Graphs—0
Integrating Controllable Motion Skills from Demonstrations—0
Full error analysis of policy gradient learning algorithms for exploratory linear quadratic mean-field control problem in continuous time with common noise—0
Active Sensing of Knee Osteoarthritis Progression with Reinforcement Learning—0
Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response—0
Visual Grounding for Object-Level Generalization in Reinforcement LearningCode1
Coordinating Planning and Tracking in Layered Control Policies via Actor-Critic LearningCode0
TCR-GPT: Integrating Autoregressive Model and Reinforcement Learning for T-Cell Receptor Repertoires Generation—0
Multi-Objective Deep Reinforcement Learning for Optimisation in Autonomous Systems—0
A Survey on Self-play Methods in Reinforcement Learning—0
Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment—0
On the Perturbed States for Transformed Input-robust Reinforcement LearningCode0
Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks—0
Multi-agent Assessment with QoS Enhancement for HD Map Updates in a Vehicular Network—0
ProSpec RL: Plan Ahead, then Execute—0
Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations—0
Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems—0
Evolution of cooperation in the public goods game with Q-learning—0
A Method for Fast Autonomy Transfer in Reinforcement Learning—0
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
Appraisal-Guided Proximal Policy Optimization: Modeling Psychological Disorders in Dynamic Grid World—0
Anomalous State Sequence Modeling to Enhance Safety in Reinforcement Learning—0
NAVIX: Scaling MiniGrid Environments with JAXCode2
Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solvers—0
QT-TDM: Planning With Transformer Dynamics Model and Autoregressive Q-Learning—0
Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning—0
Pretrained Visual Representations in Reinforcement Learning—0
Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems—0
Gymnasium: A Standard Interface for Reinforcement Learning EnvironmentsCode11
Path Following and Stabilisation of a Bicycle Model using a Reinforcement Learning Approach—0
SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning—0
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement LearningCode2
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Benchmark Results

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