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 1821–1830 of 15113 papers

TitleStatusHype
Fashion Captioning: Towards Generating Accurate Descriptions with Semantic RewardsCode1
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without SacrificesCode1
Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement LearningCode1
Robust Deep Reinforcement Learning through Adversarial LossCode1
Robust Reinforcement Learning using Adversarial PopulationsCode1
Reinforced Epidemic Control: Saving Both Lives and EconomyCode1
PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to RewardsCode1
Munchausen Reinforcement LearningCode1
Multi-Step Reinforcement Learning for Single Image Super-ResolutionCode1
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Benchmark Results

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