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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 121–130 of 1918 papers

Title	Date	Tasks	Status	Hype
FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning Techniques	Mar 21, 2020	Q-Learningreinforcement-learning	CodeCode Available	1
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction	Mar 16, 2020	Deep Reinforcement LearningMeta-Learning	CodeCode Available	1
FACMAC: Factored Multi-Agent Centralised Policy Gradients	Mar 14, 2020	MuJoCoMulti-agent Reinforcement Learning	CodeCode Available	1
Optimistic Exploration even with a Pessimistic Initialisation	Feb 26, 2020	Efficient ExplorationQ-Learning	CodeCode Available	1
Maxmin Q-learning: Controlling the Estimation Bias of Q-learning	Feb 16, 2020	Q-Learning	CodeCode Available	1
A Stochastic Game Framework for Efficient Energy Management in Microgrid Networks	Feb 6, 2020	energy managementenergy trading	CodeCode Available	1
Discriminator Soft Actor Critic without Extrinsic Rewards	Jan 19, 2020	Imitation LearningQ-Learning	CodeCode Available	1
An Optimistic Perspective on Offline Deep Reinforcement Learning	Jan 1, 2020	Atari GamesDeep Reinforcement Learning	CodeCode Available	1
Benchmarking Batch Deep Reinforcement Learning Algorithms	Oct 3, 2019	BenchmarkingDeep Reinforcement Learning	CodeCode Available	1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?	Sep 26, 2019	Feature EngineeringQ-Learning	CodeCode Available	1

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