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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 1526–1550 of 1918 papers

TitleStatusHype
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
A Deep Learning Approach to Grasping the InvisibleCode0
Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning—0
A Multistep Lyapunov Approach for Finite-Time Analysis of Biased Stochastic Approximation—0
Q-Learning Based Aerial Base Station Placement for Fairness Enhancement in Mobile Networks—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
Encoders and Decoders for Quantum Expander Codes Using Machine Learning—0
Gradient Q(σ, λ): A Unified Algorithm with Function Approximation for Reinforcement Learning—0
Q-DATA: Enhanced Traffic Flow Monitoring in Software-Defined Networks applying Q-learning—0
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
Networked Control of Nonlinear Systems under Partial Observation Using Continuous Deep Q-Learning—0
STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control—0
Deep Reinforcement Learning for Foreign Exchange Trading—0
Performing Deep Recurrent Double Q-Learning for Atari GamesCode0
Learn How to Cook a New Recipe in a New House: Using Map Familiarization, Curriculum Learning, and Bandit Feedback to Learn Families of Text-Based Adventure GamesCode0
Large-Scale Traffic Signal Control Using a Novel Multi-Agent Reinforcement Learning—0
Batch Recurrent Q-Learning for Backchannel Generation Towards Engaging Agents—0
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