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 3561–3570 of 15113 papers

TitleStatusHype
Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning—0
Reinforcement Learning Discovers Efficient Decentralized Graph Path Search StrategiesCode0
Hand-Object Interaction Pretraining from Videos—0
Digital Twin for Autonomous Guided Vehicles based on Integrated Sensing and Communications—0
Optimal Management of Grid-Interactive Efficient Buildings via Safe Reinforcement Learning—0
Learning Efficient Recursive Numeral Systems via Reinforcement Learning—0
Online Decision MetaMorphFormer: A Casual Transformer-Based Reinforcement Learning Framework of Universal Embodied Intelligence—0
The Role of Deep Learning Regularizations on Actors in Offline RLCode0
Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement LearningCode0
Superior Computer Chess with Model Predictive Control, Reinforcement Learning, and Rollout—0
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Benchmark Results

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