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 2081–2090 of 15113 papers

TitleStatusHype
MushroomRL: Simplifying Reinforcement Learning ResearchCode1
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot ControlCode1
Bridging the Gap Between f-GANs and Wasserstein GANsCode1
Meta Reinforcement Learning with Autonomous Inference of Subtask DependenciesCode1
Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement LearningCode1
Variational Imitation Learning with Diverse-quality DemonstrationsCode1
An Optimistic Perspective on Offline Deep Reinforcement LearningCode1
CURL: Contrastive Unsupervised Representation Learning for Reinforcement LearningCode1
PAC Confidence Sets for Deep Neural Networks via Calibrated PredictionCode1
Pseudo Random Number Generation: a Reinforcement Learning approachCode1
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Benchmark Results

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