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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 18011850 of 1918 papers

TitleStatusHype
Supervised Q-walk for Learning Vector Representation of Nodes in Networks0
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot NavigationCode0
A Simple Reinforcement Learning Mechanism for Resource Allocation in LTE-A Networks with Markov Decision Process and Q-Learning0
An Optimal Online Method of Selecting Source Policies for Reinforcement Learning0
Improving Search through A3C Reinforcement Learning based Conversational Agent0
Automated Cloud Provisioning on AWS using Deep Reinforcement LearningCode1
BIBI System Description: Building with CNNs and Breaking with Deep Reinforcement Learning0
Constructing narrative using a generative model and continuous action policies0
Multi-Agent Q-Learning for Minimizing Demand-Supply Power Deficit in Microgrids0
Practical Block-wise Neural Network Architecture GenerationCode0
Investigating Reinforcement Learning Agents for Continuous State Space Environments0
Guiding Reinforcement Learning Exploration Using Natural Language0
On-line Building Energy Optimization using Deep Reinforcement Learning0
Empirical evaluation of a Q-Learning Algorithm for Model-free Autonomous Soaring0
Fastest Convergence for Q-learning0
Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells0
Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement0
A Self-Adaptive Proposal Model for Temporal Action Detection based on Reinforcement LearningCode0
Reinforcement Learning under Model Mismatch0
Learning to Learn from Noisy Web Videos0
Generalized Value Iteration Networks: Life Beyond LatticesCode0
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive EnvironmentsCode1
UCB Exploration via Q-Ensembles0
Implications of Decentralized Q-learning Resource Allocation in Wireless NetworksCode0
Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning0
A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling0
Identification and Off-Policy Learning of Multiple Objectives Using Adaptive Clustering0
Learning to Represent Haptic Feedback for Partially-Observable Tasks0
Learning Hard Alignments with Variational Inference0
Discrete Sequential Prediction of Continuous Actions for Deep RL0
Policy Iterations for Reinforcement Learning Problems in Continuous Time and Space -- Fundamental Theory and MethodsCode0
Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning0
Equivalence Between Policy Gradients and Soft Q-Learning0
Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads0
Deep Q-learning from DemonstrationsCode0
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation0
Pseudorehearsal in value function approximation0
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing0
Evolution Strategies as a Scalable Alternative to Reinforcement LearningCode1
Multi-step Reinforcement Learning: A Unifying Algorithm0
Bridging the Gap Between Value and Policy Based Reinforcement Learning0
Stabilising Experience Replay for Deep Multi-Agent Reinforcement LearningCode1
Reinforcement Learning with Deep Energy-Based PoliciesCode0
Learning Control for Air Hockey Striking using Deep Reinforcement Learning0
Collaborative Deep Reinforcement Learning for Joint Object Search0
The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI0
FPGA Architecture for Deep Learning and its application to Planetary Robotics0
Learning to predict where to look in interactive environments using deep recurrent q-learning0
Playing Doom with SLAM-Augmented Deep Reinforcement LearningCode0
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy CriticCode0
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