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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 411–420 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
Provably efficient RL with Rich Observations via Latent State Decoding	Jan 25, 2019	ClusteringQ-Learning	CodeCode Available	0	5
Deep Ordinal Reinforcement Learning	May 6, 2019	Deep Reinforcement LearningOpenAI Gym	CodeCode Available	0	5
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms	Sep 1, 2019	Deep Reinforcement LearningQ-Learning	CodeCode Available	0	5
Q-Distribution guided Q-learning for offline reinforcement learning: Uncertainty penalized Q-value via consistency model	Oct 27, 2024	D4RLQ-Learning	CodeCode Available	0	5
A Comparison of Reward Functions in Q-Learning Applied to a Cart Position Problem	May 25, 2021	PositionQ-Learning	CodeCode Available	0	5
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings	Oct 29, 2020	Change Point DetectionOff-policy evaluation	CodeCode Available	0	5
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic	Nov 7, 2016	continuous-controlContinuous Control	CodeCode Available	0	5
Deep Q-learning: a robust control approach	Jan 21, 2022	OpenAI GymQ-Learning	CodeCode Available	0	5
Deep Quality-Value (DQV) Learning	Sep 30, 2018	Atari GamesDeep Reinforcement Learning	CodeCode Available	0	5
Automatic Data Augmentation by Learning the Deterministic Policy	Oct 18, 2019	Data AugmentationDeep Reinforcement Learning	CodeCode Available	0	5

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