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 876–900 of 15113 papers

TitleStatusHype
Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control—0
Statistical Inference in Reinforcement Learning: A Selective SurveyCode0
Together We Rise: Optimizing Real-Time Multi-Robot Task Allocation using Coordinated Heterogeneous Plays—0
An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning—0
Hyperspherical Normalization for Scalable Deep Reinforcement Learning—0
On the Design of Safe Continual RL Methods for Control of Nonlinear SystemsCode0
The Evolving Landscape of LLM- and VLM-Integrated Reinforcement Learning—0
Generating π-Functional Molecules Using STGG+ with Active LearningCode1
Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models—0
Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement LearningCode7
Reinforcement Learning for Ultrasound Image Analysis A Comprehensive Review of Advances and Applications—0
Reinforcement Learning with Graph Attention for Routing and Wavelength Assignment with Lightpath Reuse—0
Discovering highly efficient low-weight quantum error-correcting codes with reinforcement learning—0
MLGym: A New Framework and Benchmark for Advancing AI Research Agents—0
Optimizing Gene-Based Testing for Antibiotic Resistance Prediction—0
Comprehensive Review on the Control of Heat Pumps for Energy Flexibility in Distribution Networks—0
Uncertainty quantification for Markov chains with application to temporal difference learning—0
Hierarchical RL-MPC for Demand Response Scheduling—0
SPPD: Self-training with Process Preference Learning Using Dynamic Value Margin—0
EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning—0
Demystifying Multilingual Chain-of-Thought in Process Reward Modeling—0
LocalEscaper: A Weakly-supervised Framework with Regional Reconstruction for Scalable Neural TSP Solvers—0
Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex TasksCode0
Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage PlanningCode0
RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning—0
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Benchmark Results

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