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 881–890 of 15113 papers

TitleStatusHype
Hyperspherical Normalization for Scalable Deep Reinforcement Learning—0
On the Design of Safe Continual RL Methods for Control of Nonlinear SystemsCode0
Generating π-Functional Molecules Using STGG+ with Active LearningCode1
Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement LearningCode7
Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models—0
Reinforcement Learning for Ultrasound Image Analysis A Comprehensive Review of Advances and Applications—0
Reinforcement Learning with Graph Attention for Routing and Wavelength Assignment with Lightpath Reuse—0
MLGym: A New Framework and Benchmark for Advancing AI Research Agents—0
Discovering highly efficient low-weight quantum error-correcting codes with reinforcement learning—0
Optimizing Gene-Based Testing for Antibiotic Resistance Prediction—0
Show:102550
← PrevPage 89 of 1512Next →

Benchmark Results

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