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 3671–3680 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Multi-user Massive MIMO with Channel Aging—0
Deep Reinforcement Learning for Navigation in AAA Video Games—0
Deep Reinforcement Learning for Neural Control—0
Deep Reinforcement Learning for NLP—0
Deep Reinforcement Learning for On-line Dialogue State Tracking—0
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems—0
Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations—0
Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer—0
Deep Reinforcement Learning for Online Error Detection in Cyber-Physical Systems—0
Differentially Private Exploration in Reinforcement Learning with Linear Representation—0
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Benchmark Results

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