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 3341–3350 of 15113 papers

TitleStatusHype
AI-based traffic analysis in digital twin networks—0
Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory—0
Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation—0
Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data CorruptionsCode0
A Review of Reinforcement Learning in Financial Applications—0
Effective ML Model Versioning in Edge Networks—0
Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language UseCode0
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs—0
Noise as a Double-Edged Sword: Reinforcement Learning Exploits Randomized Defenses in Neural Networks—0
EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization—0
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Benchmark Results

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