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 801–825 of 15113 papers

TitleStatusHype
VisRL: Intention-Driven Visual Perception via Reinforced ReasoningCode1
MM-Eureka: Exploring Visual Aha Moment with Rule-based Large-scale Reinforcement LearningCode4
Probabilistic Shielding for Safe Reinforcement Learning—0
Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba ModelsCode0
Agent models: Internalizing Chain-of-Action Generation into Reasoning modelsCode2
Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language ModelsCode5
A Novel Multi-Objective Reinforcement Learning Algorithm for Pursuit-Evasion Game—0
Automated Proof of Polynomial Inequalities via Reinforcement LearningCode0
UAV-Assisted Coverage Hole Detection Using Reinforcement Learning in Urban Cellular Networks—0
GFlowVLM: Enhancing Multi-step Reasoning in Vision-Language Models with Generative Flow Networks—0
Dynamic Load Balancing for EV Charging Stations Using Reinforcement Learning and Demand Prediction—0
ULTHO: Ultra-Lightweight yet Efficient Hyperparameter Optimization in Deep Reinforcement Learning—0
Vairiational Stochastic Games—0
Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security—0
Guaranteeing Out-Of-Distribution Detection in Deep RL via Transition Estimation—0
Policy Constraint by Only Support Constraint for Offline Reinforcement LearningCode0
R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement LearningCode4
Generative Multi-Agent Q-Learning for Policy Optimization: Decentralized Wireless Networks—0
Tractable Representations for Convergent Approximation of Distributional HJB Equations—0
Multi-Fidelity Policy Gradient Algorithms—0
Multi-Robot Collaboration through Reinforcement Learning and Abstract Simulation—0
Can We Optimize Deep RL Policy Weights as Trajectory Modeling?—0
Energy-Weighted Flow Matching for Offline Reinforcement Learning—0
Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A reviewCode0
Data-Efficient Learning from Human Interventions for Mobile Robots—0
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Benchmark Results

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