Sign in
ICML 2020
Event Home
Q-Learning Algorithm for Mean-Field Controls, with Convergence and Complexity Analysis (ICML 2020 Workshop)
Jul 14, 2020
|
46 views
|
arXiv link
Haotian Gu
Follow
Multi-agent Reinforcement Learning
31 videos · undefined sub area
Details
Theoretical Foundations of Reinforcement Learning Paper: Q-Learning Algorithm for Mean-Field Controls, with Convergence and Complexity Analysis Author: Haotian Gu (UC Berkeley), Xin Guo (UC Berkeley), Xiaoli Wei (UC Berkeley), Renyuan Xu (Oxford) Link:
https://arxiv.org/pdf/2002.04131.pdf
Category: ICML 2020
Comments
loading...
Reactions
(0)
| Note
📝 No reactions yet
Be the first one to share your thoughts!
Reactions
(0)
Note
loading...
Recommended
46:16
ICAPS 2012: Awards & Best Dissertation Presentations
ICAPS
| Aug 19, 2014
19:31
ICAPS 2014: Miquel Ramírez on "Directed Fixed-Point Regression-Based Planning..."
ICAPS
| Sep 22, 2014
26:22
Representation Learning: A Review and New Perspectives
AISTATS 2014
| Nov 12, 2014
1:43
Playing Atari with Deep Reinforcement Learning | Two Minute Paper
Two Minute Papers
| Mar 7, 2015
20:54
ICAPS 2014: Menkes van den Briel on "Flow-based Heuristics for Optimal Planning"
ICAPS
| Mar 13, 2015