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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 931–940 of 1918 papers

TitleStatusHype
Can Q-learning solve Multi Armed Bantids?—0
Playing 2048 With Reinforcement LearningCode0
Balancing Value Underestimation and Overestimation with Realistic Actor-CriticCode0
A Q-Learning-based Approach for Distributed Beam Scheduling in mmWave Networks—0
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs—0
Value Penalized Q-Learning for Recommender Systems—0
Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication—0
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning—0
Fast Block Linear System Solver Using Q-Learning Schduling for Unified Dynamic Power System Simulations—0
Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games—0
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