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 2641–2650 of 15113 papers

TitleStatusHype
Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning—0
Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing—0
AlphaSeq: Sequence Discovery with Deep Reinforcement Learning—0
A Coarse to Fine Question Answering System based on Reinforcement Learning—0
Correlation Priors for Reinforcement Learning—0
Automating Turbulence Modeling by Multi-Agent Reinforcement Learning—0
Automating the resolution of flight conflicts: Deep reinforcement learning in service of air traffic controllers—0
AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search—0
Automating Staged Rollout with Reinforcement Learning—0
Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning—0
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Benchmark Results

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