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

Showing 20312040 of 15113 papers

TitleStatusHype
Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement LearningCode1
Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement LearningCode1
Discriminative Particle Filter Reinforcement Learning for Complex Partial ObservationsCode1
Reinforcement Learning Framework for Deep Brain Stimulation StudyCode1
How To Avoid Being Eaten By a Grue: Exploration Strategies for Text-Adventure AgentsCode1
Sim2Real Transfer for Reinforcement Learning without Dynamics RandomizationCode1
Reinforcement Learning for Molecular Design Guided by Quantum MechanicsCode1
Generating Automatic Curricula via Self-Supervised Active Domain RandomizationCode1
Kalman meets Bellman: Improving Policy Evaluation through Value TrackingCode1
Reinforced active learning for image segmentationCode1
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Benchmark Results

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