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 3601–3610 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography—0
Deep Reinforcement Learning for Day-to-day Dynamic Tolling in Tradable Credit Schemes—0
Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey—0
Automatic Text Summarization Using Reinforcement Learning with Embedding Features—0
Deep Reinforcement Learning for DER Cyber-Attack Mitigation—0
Deep Reinforcement Learning for Detecting Malicious Websites—0
CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning—0
Deep Reinforcement Learning for Dexterous Manipulation with Concept Networks—0
Deep Reinforcement Learning for Distributed Uncoordinated Cognitive Radios Resource Allocation—0
Cost-Effective Two-Stage Network Slicing for Edge-Cloud Orchestrated Vehicular Networks—0
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Benchmark Results

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