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 3371–3380 of 15113 papers

TitleStatusHype
Cover Tree Bayesian Reinforcement Learning—0
A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning—0
Accelerating the Learning of TAMER with Counterfactual Explanations—0
Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks—0
Deep Reinforcement Learning from Policy-Dependent Human Feedback—0
Augmented Lagrangian-Based Safe Reinforcement Learning Approach for Distribution System Volt/VAR Control—0
DEEP ADVERSARIAL FORWARD MODEL—0
Deep Adversarial Reinforcement Learning for Object Disentangling—0
DeepAGREL: Biologically plausible deep learning via direct reinforcement—0
Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles—0
Show:102550
← PrevPage 338 of 1512Next →

Benchmark Results

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