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 1011–1020 of 15113 papers

TitleStatusHype
Exploratory Mean-Variance Portfolio Optimization with Regime-Switching Market Dynamics—0
Safe Reinforcement Learning for Real-World Engine Control—0
Challenges in Ensuring AI Safety in DeepSeek-R1 Models: The Shortcomings of Reinforcement Learning Strategies—0
Improving Vision-Language-Action Model with Online Reinforcement Learning—0
Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning—0
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training—0
xJailbreak: Representation Space Guided Reinforcement Learning for Interpretable LLM JailbreakingCode1
MPC4RL -- A Software Package for Reinforcement Learning based on Model Predictive Control—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