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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

Showing 18511900 of 1918 papers

TitleStatusHype
Designing Neural Network Architectures using Reinforcement LearningCode0
Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality TighteningCode0
A Differentiable Physics Engine for Deep Learning in Robotics0
Combining policy gradient and Q-learning0
Using a Deep Reinforcement Learning Agent for Traffic Signal Control0
Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear0
Internet of Things Applications: Animal Monitoring with Unmanned Aerial Vehicle0
Active exploration in parameterized reinforcement learningCode0
Modelling Stock-market Investors as Reinforcement Learning Agents [Correction]0
Playing FPS Games with Deep Reinforcement LearningCode0
Interactive Spoken Content Retrieval by Deep Reinforcement Learning0
3D Simulation for Robot Arm Control with Deep Q-Learning0
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks0
Q-Learning with Basic Emotions0
Multi Exit Configuration of Mesoscopic Pedestrian Simulation0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems0
Learning to Communicate with Deep Multi-Agent Reinforcement LearningCode0
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement LearningCode0
Neurohex: A Deep Q-learning Hex Agent0
Continuous Deep Q-Learning with Model-based AccelerationCode1
Reinforcement Learning approach for Real Time Strategy Games Battle city and S30
Using Deep Q-Learning to Control Optimization Hyperparameters0
Angrier Birds: Bayesian reinforcement learningCode0
Taming the Noise in Reinforcement Learning via Soft UpdatesCode0
Increasing the Action Gap: New Operators for Reinforcement LearningCode0
Q-Networks for Binary Vector Actions0
Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions0
Robotic Search & Rescue via Online Multi-task Reinforcement Learning0
Multiagent Cooperation and Competition with Deep Reinforcement LearningCode1
Learning Simple Algorithms from ExamplesCode0
Deep Reinforcement Learning with a Natural Language Action SpaceCode0
A disembodied developmental robotic agent called Samu BátfaiCode0
Two Phase Q-learning for Bidding-based Vehicle Sharing0
Deep Reinforcement Learning with Double Q-learningCode1
Optimization of anemia treatment in hemodialysis patients via reinforcement learning0
Continuous control with deep reinforcement learningCode1
Distributed Deep Q-Learning0
Artificial Prediction Markets for Online Prediction of Continuous Variables-A Preliminary Report0
Deep Recurrent Q-Learning for Partially Observable MDPsCode1
Online Transfer Learning in Reinforcement Learning Domains0
Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge EvolutionCode0
Decentralized Q-Learning for Stochastic Teams and Games0
Autonomous CRM Control via CLV Approximation with Deep Reinforcement Learning in Discrete and Continuous Action Space0
Energy Sharing for Multiple Sensor Nodes with Finite Buffers0
Correct-by-synthesis reinforcement learning with temporal logic constraints0
Empirical Q-Value Iteration0
Q-learning for Optimal Control of Continuous-time Systems0
Learning to Cooperate via Policy Search0
Reinforcement Learning Based Algorithm for the Maximization of EV Charging Station Revenue0
Personalized Medical Treatments Using Novel Reinforcement Learning Algorithms0
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