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 1601–1625 of 15113 papers

TitleStatusHype
AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language ModelsCode1
Optimal Management of Grid-Interactive Efficient Buildings via Safe Reinforcement Learning—0
Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning—0
Digital Twin for Autonomous Guided Vehicles based on Integrated Sensing and Communications—0
Reinforcement Learning Discovers Efficient Decentralized Graph Path Search StrategiesCode0
Hand-Object Interaction Pretraining from Videos—0
The Role of Deep Learning Regularizations on Actors in Offline RLCode0
Learning Efficient Recursive Numeral Systems via Reinforcement Learning—0
Online Decision MetaMorphFormer: A Casual Transformer-Based Reinforcement Learning Framework of Universal Embodied Intelligence—0
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
Automated Data Augmentation for Few-Shot Time Series Forecasting: A Reinforcement Learning Approach Guided by a Model Zoo—0
BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design—0
Markov Chain Variance Estimation: A Stochastic Approximation Approach—0
Forward KL Regularized Preference Optimization for Aligning Diffusion Policies—0
BAMDP Shaping: a Unified Theoretical Framework for Intrinsic Motivation and Reward Shaping—0
Semifactual Explanations for Reinforcement LearningCode0
An Introduction to Quantum Reinforcement Learning (QRL)—0
Causality-Driven Reinforcement Learning for Joint Communication and Sensing—0
Reinforcement Learning for Rate Maximization in IRS-aided OWC Networks—0
Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy ChurnCode0
Reward-Directed Score-Based Diffusion Models via q-Learning—0
Sample and Oracle Efficient Reinforcement Learning for MDPs with Linearly-Realizable Value Functions—0
Reinforcement Learning-Based Adaptive Load Balancing for Dynamic Cloud Environments—0
Reward Guidance for Reinforcement Learning Tasks Based on Large Language Models: The LMGT Framework—0
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Benchmark Results

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