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 8201–8225 of 15113 papers

TitleStatusHype
Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning—0
Objective-aware Traffic Simulation via Inverse Reinforcement Learning—0
Towards a Sample Efficient Reinforcement Learning Pipeline for Vision Based Robotics—0
Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point DetectionCode1
A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning—0
Deep Reinforcement Learning for Optimal Stopping with Application in Financial EngineeringCode0
Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy OptimizationCode1
Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images—0
Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement LearningCode1
Online Adaptive Optimal Control Algorithm Based on Synchronous Integral Reinforcement Learning With Explorations—0
Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning—0
Reinforcement Learning Assisted Oxygen Therapy for COVID-19 Patients Under Intensive Care—0
Application of deep reinforcement learning for Indian stock trading automation—0
Learning and Information in Stochastic Networks and Queues—0
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team CompositionCode1
Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education—0
Adaptive ABAC Policy Learning: A Reinforcement Learning Approach—0
Online Multimodal Transportation Planning using Deep Reinforcement Learning—0
PoBRL: Optimizing Multi-Document Summarization by Blending Reinforcement Learning Policies—0
Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization—0
Sublinear Least-Squares Value Iteration via Locality Sensitive Hashing—0
Reinforcement Learning for Adaptive Video Compressive Sensing—0
Meta-Reinforcement Learning by Tracking Task Non-stationarityCode0
RL-GRIT: Reinforcement Learning for Grammar Inference—0
Uncertainty Weighted Actor-Critic for Offline Reinforcement LearningCode1
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Benchmark Results

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