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 2391–2400 of 15113 papers

TitleStatusHype
Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning—0
A Comparison of learning algorithms on the Arcade Learning Environment—0
Constrained-Space Optimization and Reinforcement Learning for Complex Tasks—0
Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation—0
Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology—0
Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systems—0
A Multi-Objective Deep Reinforcement Learning Framework—0
Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning—0
Barrier-Certified Adaptive Reinforcement Learning with Applications to Brushbot Navigation—0
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning—0
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Benchmark Results

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