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 3051–3060 of 15113 papers

TitleStatusHype
Challenges in Ensuring AI Safety in DeepSeek-R1 Models: The Shortcomings of Reinforcement Learning Strategies—0
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training—0
Exploratory Mean-Variance Portfolio Optimization with Regime-Switching Market Dynamics—0
RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled PlatformsCode0
Improving Vision-Language-Action Model with Online Reinforcement Learning—0
Integrating Reinforcement Learning and AI Agents for Adaptive Robotic Interaction and Assistance in Dementia Care—0
Flexible Blood Glucose Control: Offline Reinforcement Learning from Human Feedback—0
Towards General-Purpose Model-Free Reinforcement Learning—0
FuzzyLight: A Robust Two-Stage Fuzzy Approach for Traffic Signal Control Works in Real Cities—0
Selective Experience Sharing in Reinforcement Learning Enhances Interference Management—0
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Benchmark Results

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