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 4321–4330 of 15113 papers

TitleStatusHype
A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning—0
A Dual-Memory Architecture for Reinforcement Learning on Neuromorphic Platforms—0
A Dual Memory Structure for Efficient Use of Replay Memory in Deep Reinforcement Learning—0
A dual mode adaptive basal-bolus advisor based on reinforcement learning—0
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection—0
Advanced Scaling Methods for VNF deployment with Reinforcement Learning—0
Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning—0
Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review—0
Advances in Preference-based Reinforcement Learning: A Review—0
Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning—0
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Benchmark Results

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