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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 1826–1850 of 1918 papers

TitleStatusHype
Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells—0
A Self-Adaptive Proposal Model for Temporal Action Detection based on Reinforcement LearningCode0
Reinforcement Learning under Model Mismatch—0
Learning to Learn from Noisy Web Videos—0
Generalized Value Iteration Networks: Life Beyond LatticesCode0
UCB Exploration via Q-Ensembles—0
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 learning—0
A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling—0
Learning to Represent Haptic Feedback for Partially-Observable Tasks—0
Identification and Off-Policy Learning of Multiple Objectives Using Adaptive Clustering—0
Learning Hard Alignments with Variational Inference—0
Discrete Sequential Prediction of Continuous Actions for Deep RL—0
Policy Iterations for Reinforcement Learning Problems in Continuous Time and Space -- Fundamental Theory and MethodsCode0
Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning—0
Equivalence Between Policy Gradients and Soft Q-Learning—0
Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads—0
Deep Q-learning from DemonstrationsCode0
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation—0
Pseudorehearsal in value function approximation—0
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing—0
Multi-step Reinforcement Learning: A Unifying Algorithm—0
Bridging the Gap Between Value and Policy Based Reinforcement Learning—0
Reinforcement Learning with Deep Energy-Based PoliciesCode0
Learning Control for Air Hockey Striking using Deep Reinforcement Learning—0
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