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 2151–2200 of 15113 papers

TitleStatusHype
Learning to Communicate Functional States with Nonverbal Expressions for Improved Human-Robot CollaborationCode0
Pessimistic Value Iteration for Multi-Task Data Sharing in Offline Reinforcement LearningCode1
Reinforcement Learning Problem Solving with Large Language Models—0
Towards Generalizable Agents in Text-Based Educational Environments: A Study of Integrating RL with LLMs—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
Reinforcement Learning with Adaptive Regularization for Safe Control of Critical SystemsCode0
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
Fairness Incentives in Response to Unfair Dynamic Pricing—0
Explicit Lipschitz Value Estimation Enhances Policy Robustness Against Perturbation—0
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy DataCode1
Multi-view Disentanglement for Reinforcement Learning with Multiple CamerasCode0
Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories—0
An Offline Reinforcement Learning Algorithm Customized for Multi-Task Fusion in Large-Scale Recommender Systems—0
Data-Incremental Continual Offline Reinforcement Learning—0
Reinforcement Learning Approach for Integrating Compressed Contexts into Knowledge Graphs—0
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource AllocationCode2
Continuous-time Risk-sensitive Reinforcement Learning via Quadratic Variation Penalty—0
TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning AgentsCode0
Actor-Critic Reinforcement Learning with Phased Actor—0
Physics-informed Actor-Critic for Coordination of Virtual Inertia from Power Distribution Systems—0
Prompt Optimizer of Text-to-Image Diffusion Models for Abstract Concept Understanding—0
Learn to Tour: Operator Design For Solution Feasibility Mapping in Pickup-and-delivery Traveling Salesman Problem—0
LTL-Constrained Policy Optimization with Cycle Experience Replay—0
Sustainability of Data Center Digital Twins with Reinforcement LearningCode2
Course Recommender Systems Need to Consider the Job MarketCode0
Automated Discovery of Functional Actual Causes in Complex Environments—0
What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement LearningCode0
Achieving Constant Regret in Linear Markov Decision Processes—0
Offline Trajectory Generalization for Offline Reinforcement Learning—0
Simplex Decomposition for Portfolio Allocation Constraints in Reinforcement Learning—0
The Feasibility of Constrained Reinforcement Learning Algorithms: A Tutorial Study—0
Autonomous Path Planning for Intercostal Robotic Ultrasound Imaging Using Reinforcement Learning—0
Effective Reinforcement Learning Based on Structural Information Principles—0
Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement LearningCode0
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning—0
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Benchmark Results

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