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 1341–1350 of 15113 papers

TitleStatusHype
A Review of Reinforcement Learning in Financial Applications—0
Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation—0
AI-based traffic analysis in digital twin networks—0
Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory—0
Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data CorruptionsCode0
Effective ML Model Versioning in Edge Networks—0
EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization—0
Scalable Reinforcement Post-Training Beyond Static Human Prompts: Evolving Alignment via Asymmetric Self-Play—0
Maximum Entropy Hindsight Experience Replay—0
Deterministic Exploration via Stationary Bellman Error Maximization—0
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Benchmark Results

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