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 2151–2175 of 15113 papers

TitleStatusHype
Pessimistic Value Iteration for Multi-Task Data Sharing in Offline Reinforcement LearningCode1
Learning to Communicate Functional States with Nonverbal Expressions for Improved Human-Robot CollaborationCode0
Towards Generalizable Agents in Text-Based Educational Environments: A Study of Integrating RL with LLMs—0
Reinforcement Learning Problem Solving with Large Language Models—0
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty—0
Control Policy Correction Framework for Reinforcement Learning-based Energy Arbitrage Strategies—0
EEG_RL-Net: Enhancing EEG MI Classification through Reinforcement Learning-Optimised Graph Neural Networks—0
Generalize by Touching: Tactile Ensemble Skill Transfer for Robotic Furniture Assembly—0
Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review—0
Enhancing Privacy and Security of Autonomous UAV Navigation—0
Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks—0
REBEL: Reinforcement Learning via Regressing Relative RewardsCode2
Offline Reinforcement Learning with Behavioral Supervisor Tuning—0
A fast balance optimization approach for charging enhancement of lithium-ion battery packs through deep reinforcement learningCode1
ActiveRIR: Active Audio-Visual Exploration for Acoustic Environment Modeling—0
GRSN: Gated Recurrent Spiking Neurons for POMDPs and MARL—0
DPO: A Differential and Pointwise Control Approach to Reinforcement Learning—0
Planning the path with Reinforcement Learning: Optimal Robot Motion Planning in RoboCup Small Size League EnvironmentsCode0
An MRP Formulation for Supervised Learning: Generalized Temporal Difference Learning Models—0
Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem—0
Impedance Matching: Enabling an RL-Based Running Jump in a Quadruped Robot—0
Reinforcement Learning with Adaptive Regularization for Safe Control of Critical SystemsCode0
Fairness Incentives in Response to Unfair Dynamic Pricing—0
Explicit Lipschitz Value Estimation Enhances Policy Robustness Against Perturbation—0
Multi-view Disentanglement for Reinforcement Learning with Multiple CamerasCode0
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Benchmark Results

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