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 10601–10625 of 15113 papers

TitleStatusHype
Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod RobotCode1
Learning and Reasoning for Robot Dialog and Navigation Tasks—0
Finite-sample Analysis of Greedy-GQ with Linear Function Approximation under Markovian Noise—0
Deep Reinforcement Learning for High Level Character Control—0
A reinforcement learning based decision support system in textile manufacturing process—0
Reinforcement Learning for Variable Selection in a Branch and Bound Algorithm—0
Mirror Descent Policy OptimizationCode1
Two-stage Deep Reinforcement Learning for Inverter-based Volt-VAR Control in Active Distribution Networks—0
A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments—0
Learning to Herd Agents Amongst Obstacles: Training Robust Shepherding Behaviors using Deep Reinforcement Learning—0
Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization—0
Experience Augmentation: Boosting and Accelerating Off-Policy Multi-Agent Reinforcement Learning—0
Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text—0
Reinforcement Learning for Caching with Space-Time Popularity Dynamics—0
Ultrasound Video Summarization using Deep Reinforcement LearningCode1
Privileged Information Dropout in Reinforcement Learning—0
Optimal Charging Method for Effective Li-ion Battery Life Extension Based on Reinforcement Learning—0
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency MapsCode0
Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation—0
Automating Turbulence Modeling by Multi-Agent Reinforcement Learning—0
A Simple Imitation Learning Method via Contrastive Regularization—0
Lifelong Control of Off-grid Microgrid with Model Based Reinforcement LearningCode1
Learning Transferable Concepts in Deep Reinforcement Learning—0
A Distributional View on Multi-Objective Policy Optimization—0
Think Too Fast Nor Too Slow: The Computational Trade-off Between Planning And Reinforcement LearningCode0
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Benchmark Results

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