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 3121–3130 of 15113 papers

TitleStatusHype
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC—0
Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound—0
Cross-Domain Perceptual Reward Functions—0
Crowd-PrefRL: Preference-Based Reward Learning from Crowds—0
A Survey of Explainable Reinforcement Learning—0
Crown Jewels Analysis using Reinforcement Learning with Attack Graphs—0
adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential-Decision Making Human-in-the-Loop Systems—0
Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications—0
A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective—0
Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense—0
Show:102550
← PrevPage 313 of 1512Next →

Benchmark Results

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