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 3531–3540 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning Based Dynamic Route Planning for Minimizing Travel Time—0
Deep Reinforcement Learning based Dynamic Optimization of Bus Timetable—0
Agent based modelling for continuously varying supply chains—0
PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback—0
Accelerating the Computation of UCB and Related Indices for Reinforcement Learning—0
Delayed Geometric Discounts: An Alternative Criterion for Reinforcement Learning—0
Deep Reinforcement Learning-based Image Captioning with Embedding Reward—0
Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 images—0
Delving into adversarial attacks on deep policies—0
A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning—0
Show:102550
← PrevPage 354 of 1512Next →

Benchmark Results

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