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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 1501–1525 of 1918 papers

TitleStatusHype
ModelicaGym: Applying Reinforcement Learning to Modelica ModelsCode1
Split Deep Q-Learning for Robust Object Singulation—0
ISL: A novel approach for deep explorationCode0
Joint Inference of Reward Machines and Policies for Reinforcement Learning—0
SQLR: Short-Term Memory Q-Learning for Elastic Provisioning—0
Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders—0
Q-learning Assisted Energy-Aware Traffic Offloading and Cell Switching in Heterogeneous Networks—0
Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning—0
A Deep Learning Approach to Grasping the InvisibleCode0
Q-Learning Based Aerial Base Station Placement for Fairness Enhancement in Mobile Networks—0
A Multistep Lyapunov Approach for Finite-Time Analysis of Biased Stochastic Approximation—0
Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning—0
Self-driving scale car trained by Deep reinforcement learning—0
Multi Pseudo Q-learning Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles—0
Deep Reinforcement Learning for Control of Probabilistic Boolean NetworksCode0
Gradient Q(σ, λ): A Unified Algorithm with Function Approximation for Reinforcement Learning—0
Encoders and Decoders for Quantum Expander Codes Using Machine Learning—0
Q-DATA: Enhanced Traffic Flow Monitoring in Software-Defined Networks applying Q-learning—0
rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorchCode2
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithmsCode0
Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity—0
STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control—0
Networked Control of Nonlinear Systems under Partial Observation Using Continuous Deep Q-Learning—0
Deep Reinforcement Learning for Foreign Exchange Trading—0
Performing Deep Recurrent Double Q-Learning for Atari GamesCode0
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