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 4411–4420 of 15113 papers

TitleStatusHype
A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environments—0
CUDC: A Curiosity-Driven Unsupervised Data Collection Method with Adaptive Temporal Distances for Offline Reinforcement Learning—0
Data-Driven Merton's Strategies via Policy Randomization—0
BadRL: Sparse Targeted Backdoor Attack Against Reinforcement LearningCode0
Active search and coverage using point-cloud reinforcement learning—0
Solving the swing-up and balance task for the Acrobot and Pendubot with SAC—0
Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis—0
Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints—0
Improving Environment Robustness of Deep Reinforcement Learning Approaches for Autonomous Racing Using Bayesian Optimization-based Curriculum LearningCode0
Advancing RAN Slicing with Offline Reinforcement Learning—0
Show:102550
← PrevPage 442 of 1512Next →

Benchmark Results

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