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 4431–4440 of 15113 papers

TitleStatusHype
Agent based modelling for continuously varying supply chains—0
Agent-Centric Representations for Multi-Agent Reinforcement Learning—0
Agent Environment Cycle Games—0
AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning—0
A Gentle Lecture Note on Filtrations in Reinforcement Learning—0
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning—0
Agent Probing Interaction Policies—0
Agent Spaces—0
Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound—0
Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learning—0
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Benchmark Results

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