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 4181–4190 of 15113 papers

TitleStatusHype
Reinforcement Learning and Mixed-Integer Programming for Power Plant Scheduling in Low Carbon Systems: Comparison and Hybridisation—0
Near-Optimal Differentially Private Reinforcement Learning—0
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games—0
Compiler Optimization for Quantum Computing Using Reinforcement LearningCode1
Confidence-Conditioned Value Functions for Offline Reinforcement Learning—0
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces—0
Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk—0
Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learning—0
Reinforcement Learning for Resilient Power Grids—0
Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement—0
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Benchmark Results

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