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 2926–2950 of 15113 papers

TitleStatusHype
Error-related Potential driven Reinforcement Learning for adaptive Brain-Computer Interfaces—0
FetchBot: Object Fetching in Cluttered Shelves via Zero-Shot Sim2Real—0
SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution—0
Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement LearningCode0
From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMsCode0
Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation—0
Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning—0
Survey on Strategic Mining in Blockchain: A Reinforcement Learning Approach—0
Yes, Q-learning Helps Offline In-Context RL—0
Ensemble RL through Classifier Models: Enhancing Risk-Return Trade-offs in Trading Strategies—0
Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control—0
Together We Rise: Optimizing Real-Time Multi-Robot Task Allocation using Coordinated Heterogeneous Plays—0
Statistical Inference in Reinforcement Learning: A Selective SurveyCode0
An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning—0
The Evolving Landscape of LLM- and VLM-Integrated Reinforcement Learning—0
On the Design of Safe Continual RL Methods for Control of Nonlinear SystemsCode0
Hyperspherical Normalization for Scalable Deep Reinforcement Learning—0
Reinforcement Learning with Graph Attention for Routing and Wavelength Assignment with Lightpath Reuse—0
MLGym: A New Framework and Benchmark for Advancing AI Research Agents—0
Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models—0
Discovering highly efficient low-weight quantum error-correcting codes with reinforcement learning—0
Reinforcement Learning for Ultrasound Image Analysis A Comprehensive Review of Advances and Applications—0
Comprehensive Review on the Control of Heat Pumps for Energy Flexibility in Distribution Networks—0
Optimizing Gene-Based Testing for Antibiotic Resistance Prediction—0
Uncertainty quantification for Markov chains with application to temporal difference learning—0
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Benchmark Results

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