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 1251–1260 of 15113 papers

TitleStatusHype
Faster Deep Reinforcement Learning with Slower Online NetworkCode1
Learning multiple gaits of quadruped robot using hierarchical reinforcement learningCode1
An Experimental Design Perspective on Model-Based Reinforcement LearningCode1
VMAgent: Scheduling Simulator for Reinforcement LearningCode1
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical PerspectivesCode1
Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless NetworksCode1
Tell me why! Explanations support learning relational and causal structureCode1
Hierarchical Reinforcement Learning with Timed SubgoalsCode1
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC TasksCode1
Functional Regularization for Reinforcement Learning via Learned Fourier FeaturesCode1
Show:102550
← PrevPage 126 of 1512Next →

Benchmark Results

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