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 3971–3980 of 15113 papers

TitleStatusHype
Designing Rewards for Fast Learning—0
Design of Artificial Intelligence Agents for Games using Deep Reinforcement Learning—0
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning—0
Design of Interacting Particle Systems for Fast Linear Quadratic RL—0
ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning—0
Design Principles of the Hippocampal Cognitive Map—0
Do Artificial Reinforcement-Learning Agents Matter Morally?—0
Coordinated Reinforcement Learning for Optimizing Mobile Networks—0
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention—0
Coordinated Random Access for Industrial IoT With Correlated Traffic By Reinforcement-Learning—0
Show:102550
← PrevPage 398 of 1512Next →

Benchmark Results

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