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 1701–1725 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
Value of Information and Reward Specification in Active Inference and POMDPs—0
Integrating Saliency Ranking and Reinforcement Learning for Enhanced Object DetectionCode1
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
Model-Based Transfer Learning for Contextual Reinforcement LearningCode1
Deep Reinforcement Learning for the Design of Metamaterial Mechanisms with Functional Compliance Control—0
Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear MDPs—0
Listwise Reward Estimation for Offline Preference-based 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
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Benchmark Results

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