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 4291–4300 of 15113 papers

TitleStatusHype
Domain Generalization for Robust Model-Based Offline Reinforcement Learning—0
Computational Co-Design for Variable Geometry Truss—0
An Isolation-Aware Online Virtual Network Embedding via Deep Reinforcement Learning—0
Improving Proactive Dialog Agents Using Socially-Aware Reinforcement Learning—0
Pac-Man Pete: An extensible framework for building AI in VEX RoboticsCode0
Operator Splitting Value Iteration—0
Assistive Teaching of Motor Control Tasks to HumansCode0
Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning—0
SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration—0
Explainable and Safe Reinforcement Learning for Autonomous Air MobilityCode0
Show:102550
← PrevPage 430 of 1512Next →

Benchmark Results

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