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 2111–2120 of 15113 papers

TitleStatusHype
Dominion: A New Frontier for AI Research—0
Value Augmented Sampling for Language Model Alignment and PersonalizationCode1
An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models—0
Fast Stochastic Policy Gradient: Negative Momentum for Reinforcement Learning—0
Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach—0
Improving Offline Reinforcement Learning with Inaccurate Simulators—0
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language ModelCode9
ACEGEN: Reinforcement learning of generative chemical agents for drug discoveryCode3
SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory SystemsCode0
Human-centric Reward Optimization for Reinforcement Learning-based Automated Driving using Large Language ModelsCode1
Show:102550
← PrevPage 212 of 1512Next →

Benchmark Results

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