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 7401–7425 of 15113 papers

TitleStatusHype
Combining Contention-Based Spectrum Access and Adaptive Modulation using Deep Reinforcement Learning—0
Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement LearningCode2
A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems—0
Parameter-free Reduction of the Estimation Bias in Deep Reinforcement Learning for Deterministic Policy GradientsCode0
Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability—0
Pythia: A Customizable Hardware Prefetching Framework Using Online Reinforcement LearningCode1
NICE: Robust Scheduling through Reinforcement Learning-Guided Integer ProgrammingCode1
Reinforcement Learning Under Algorithmic Triage—0
Trust Region Policy Optimisation in Multi-Agent Reinforcement LearningCode1
PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation—0
The Role of Tactile Sensing in Learning and Deploying Grasp Refinement AlgorithmsCode1
Hierarchies of Planning and Reinforcement Learning for Robot Navigation—0
Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based NavigationCode1
Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning—0
A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways—0
Deep Reinforcement Learning-Based Long-Range Autonomous Valet Parking for Smart Cities—0
Adversarial Training Blocks Generalization in Neural Policies—0
Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay—0
ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement LearningCode1
A Workflow for Offline Model-Free Robotic Reinforcement LearningCode1
A Survey on Reinforcement Learning for Recommender Systems—0
Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning—0
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic MethodsCode0
A Reinforcement Learning Benchmark for Autonomous Driving in Intersection ScenariosCode1
Introducing Symmetries to Black Box Meta Reinforcement Learning—0
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Benchmark Results

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