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

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Showing 2351–2360 of 15113 papers

TitleStatusHype
LearnAlign: Reasoning Data Selection for Reinforcement Learning in Large Language Models Based on Improved Gradient Alignment—0
Magistral—0
Viability of Future Actions: Robust Safety in Reinforcement Learning via Entropy RegularizationCode0
Shapley Machine: A Game-Theoretic Framework for N-Agent Ad Hoc TeamworkCode0
PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative Verifier—0
Automatic Treatment Planning using Reinforcement Learning for High-dose-rate Prostate Brachytherapy—0
A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications—0
Attention on flow control: transformer-based reinforcement learning for lift regulation in highly disturbed flows—0
Bridging Continuous-time LQR and Reinforcement Learning via Gradient Flow of the Bellman Error—0
Offline RL with Smooth OOD Generalization in Convex Hull and its NeighborhoodCode0
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Benchmark Results

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