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Linear Function Approximation as a Computationally Efficient Method to Solve Classical Reinforcement Learning Challenges
27 May 2024
Hari Srikanth
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Papers citing
"Linear Function Approximation as a Computationally Efficient Method to Solve Classical Reinforcement Learning Challenges"
4 / 4 papers shown
Title
The Principles of Deep Learning Theory
Daniel A. Roberts
Sho Yaida
Boris Hanin
FaML
PINN
GNN
17
245
0
18 Jun 2021
Deep Reinforcement Learning for Autonomous Driving: A Survey
B. R. Kiran
Ibrahim Sobh
V. Talpaert
Patrick Mannion
A. A. Sallab
S. Yogamani
P. Pérez
234
1,658
0
02 Feb 2020
Solving Rubik's Cube with a Robot Hand
OpenAI
Ilge Akkaya
Marcin Andrychowicz
Maciek Chociej
Ma-teusz Litwin
...
Peter Welinder
Lilian Weng
Qiming Yuan
Wojciech Zaremba
Lei Zhang
ODL
53
1,215
0
16 Oct 2019
Proximal Policy Optimization Algorithms
John Schulman
Filip Wolski
Prafulla Dhariwal
Alec Radford
Oleg Klimov
OffRL
183
18,685
0
20 Jul 2017
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