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Q-Learning

The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

( Image credit: Playing Atari with Deep Reinforcement Learning )

Papers

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Showing 926–950 of 1918 papers

TitleStatusHype
Multi-Agent Advisor Q-LearningCode0
Automating Control of Overestimation Bias for Reinforcement Learning—0
Can Q-Learning be Improved with Advice?—0
Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks—0
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow—0
Can Q-learning solve Multi Armed Bantids?—0
Playing 2048 With Reinforcement LearningCode0
Balancing Value Underestimation and Overestimation with Realistic Actor-CriticCode0
A Q-Learning-based Approach for Distributed Beam Scheduling in mmWave Networks—0
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs—0
Value Penalized Q-Learning for Recommender Systems—0
Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication—0
Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games—0
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning—0
Offline Reinforcement Learning with Implicit Q-LearningCode1
Fast Block Linear System Solver Using Q-Learning Schduling for Unified Dynamic Power System Simulations—0
Urban traffic dynamic rerouting framework: A DRL-based model with fog-cloud architecture—0
Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning—0
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization—0
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning—0
Training Transition Policies via Distribution Matching for Complex TasksCode0
Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations—0
A study of first-passage time minimization via Q-learning in heated gridworlds—0
A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing—0
Dropout Q-Functions for Doubly Efficient Reinforcement LearningCode1
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