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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 1026–1050 of 1918 papers

TitleStatusHype
Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games—0
Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning—0
Urban traffic dynamic rerouting framework: A DRL-based model with fog-cloud architecture—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
Deep reinforcement learning for guidewire navigation in coronary artery phantom—0
A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes—0
Cellular traffic offloading via Opportunistic Networking with Reinforcement Learning—0
Towards Unknown-aware Deep Q-Learning—0
Q-learning for real time control of heterogeneous microagent collectives—0
Q-Learning Scheduler for Multi-Task Learning through the use of Histogram of Task Uncertainty—0
Polyphonic Music Composition: An Adversarial Inverse Reinforcement Learning Approach—0
Text Generation with Efficient (Soft) Q-Learning—0
Bootstrapped Hindsight Experience replay with Counterintuitive Prioritization—0
Learning Explicit Credit Assignment for Multi-agent Joint Q-learning—0
Value Refinement Network (VRN)—0
Robust and Data-efficient Q-learning by Composite Value-estimation—0
^2-exploration for Reinforcement Learning—0
An Attempt to Model Human Trust with Reinforcement Learning—0
Untangling Braids with Multi-agent Q-Learning—0
Unifying Top-down and Bottom-up for Recurrent Visual Attention—0
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