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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4001–4025 of 15113 papers

TitleStatusHype
Do No Harm: A Counterfactual Approach to Safe Reinforcement Learning—0
Coordinated Reinforcement Learning for Optimizing Mobile Networks—0
Adaptive Structural Hyper-Parameter Configuration by Q-Learning—0
Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents—0
Development of collective behavior in newborn artificial agents—0
Bayesian Linear Regression on Deep Representations—0
Bayesian Nonparametric Reinforcement Learning in LTE and Wi-Fi Coexistence—0
DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation—0
Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning—0
Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance—0
Coordinated Random Access for Industrial IoT With Correlated Traffic By Reinforcement-Learning—0
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost—0
Assessing the Impact of Distribution Shift on Reinforcement Learning Performance—0
Coordinated Multi-Agent Exploration Using Shared Goals—0
Coordinated Frequency Control through Safe Reinforcement Learning—0
Assessing Policy, Loss and Planning Combinations in Reinforcement Learning using a New Modular Architecture—0
A Generalized Natural Actor-Critic Algorithm—0
Bayesian Q-learning With Imperfect Expert Demonstrations—0
Diagnosing Reinforcement Learning for Traffic Signal Control—0
Dialog Action-Aware Transformer for Dialog Policy Learning—0
Bayesian Reinforcement Learning in Factored POMDPs—0
Dialogue Evaluation with Offline Reinforcement Learning—0
Coordinated Exploration in Concurrent Reinforcement Learning—0
Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement Learning with Clairvoyant Experts—0
Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study—0
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Benchmark Results

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