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 3621–3630 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Entity Alignment—0
Developing cooperative policies for multi-stage tasks—0
Deep Reinforcement Learning for Equal Risk Pricing and Hedging under Dynamic Expectile Risk Measures—0
Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning—0
Automating Staged Rollout with Reinforcement Learning—0
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch—0
Automating the resolution of flight conflicts: Deep reinforcement learning in service of air traffic controllers—0
Deep Reinforcement Learning for Field Development Optimization—0
Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder—0
A State Augmentation based approach to Reinforcement Learning from Human Preferences—0
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Benchmark Results

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