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 3141–3150 of 15113 papers

TitleStatusHype
Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret—0
Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey—0
Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques—0
Goal-Conditioned Data Augmentation for Offline Reinforcement Learning—0
Efficient and Scalable Deep Reinforcement Learning for Mean Field Control GamesCode0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation—0
Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization—0
xSRL: Safety-Aware Explainable Reinforcement Learning -- Safety as a Product of ExplainabilityCode0
Provably Efficient Exploration in Reward Machines with Low Regret—0
A Reinforcement Learning-Based Task Mapping Method to Improve the Reliability of Clustered Manycores—0
Show:102550
← PrevPage 315 of 1512Next →

Benchmark Results

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