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 8401–8425 of 15113 papers

TitleStatusHype
Dual Behavior Regularized Reinforcement Learning—0
Lifelong Robotic Reinforcement Learning by Retaining Experiences—0
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning—0
Hindsight Foresight Relabeling for Meta-Reinforcement LearningCode0
Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation—0
Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications—0
Coordinated Random Access for Industrial IoT With Correlated Traffic By Reinforcement-Learning—0
Decentralized Global Connectivity Maintenance for Multi-Robot Navigation: A Reinforcement Learning Approach—0
Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learning—0
Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State ObservationsCode0
POAR: Efficient Policy Optimization via Online Abstract State Representation Learning—0
Soft Actor-Critic With Integer Actions—0
RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning—0
Reinforcement Learning on Encrypted Data—0
Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition—0
Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing—0
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning—0
Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning—0
Balancing detectability and performance of attacks on the control channel of Markov Decision ProcessesCode0
Back to Basics: Deep Reinforcement Learning in Traffic Signal ControlCode0
Estimation of Warfarin Dosage with Reinforcement LearningCode0
DCUR: Data Curriculum for Teaching via Samples with Reinforcement LearningCode0
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback—0
What Does The User Want? Information Gain for Hierarchical Dialogue Policy Optimisation—0
Short Quantum Circuits in Reinforcement Learning Policies for the Vehicle Routing Problem—0
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Benchmark Results

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