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 3751–3760 of 15113 papers

TitleStatusHype
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes—0
Correlation Priors for Reinforcement Learning—0
Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks—0
Auxiliary Reward Generation with Transition Distance Representation Learning—0
Assured RL: Reinforcement Learning with Almost Sure Constraints—0
Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing—0
Deep Reinforcement Learning for Visual Object Tracking in Videos—0
Deep Reinforcement Learning for Weapons to Targets Assignment in a Hypersonic strike—0
Correlation Filter Selection for Visual Tracking Using Reinforcement Learning—0
Assured Learning-enabled Autonomy: A Metacognitive Reinforcement Learning Framework—0
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Benchmark Results

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