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From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement
  Learning -- Insights from Biological Systems on Adaptive Flexibility

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility

13 August 2019
M. Schilling
Helge J. Ritter
F. Ohl
    AI4CE
ArXivPDFHTML

Papers citing "From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility"

11 / 11 papers shown
Title
AlphaStar: An Evolutionary Computation Perspective
AlphaStar: An Evolutionary Computation Perspective
Kai Arulkumaran
Antoine Cully
Julian Togelius
30
183
0
05 Feb 2019
The Value Function Polytope in Reinforcement Learning
The Value Function Polytope in Reinforcement Learning
Robert Dadashi
Adrien Ali Taïga
Nicolas Le Roux
Dale Schuurmans
Marc G. Bellemare
22
46
0
31 Jan 2019
Open-ended Learning in Symmetric Zero-sum Games
Open-ended Learning in Symmetric Zero-sum Games
David Balduzzi
M. Garnelo
Yoram Bachrach
Wojciech M. Czarnecki
Julien Perolat
Max Jaderberg
T. Graepel
42
169
0
23 Jan 2019
Human-level performance in first-person multiplayer games with
  population-based deep reinforcement learning
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Max Jaderberg
Wojciech M. Czarnecki
Iain Dunning
Luke Marris
Guy Lever
...
Joel Z Leibo
David Silver
Demis Hassabis
Koray Kavukcuoglu
T. Graepel
OffRL
68
717
0
03 Jul 2018
Addressing Function Approximation Error in Actor-Critic Methods
Addressing Function Approximation Error in Actor-Critic Methods
Scott Fujimoto
H. V. Hoof
David Meger
OffRL
153
5,121
0
26 Feb 2018
The Predictron: End-To-End Learning and Planning
The Predictron: End-To-End Learning and Planning
David Silver
H. V. Hasselt
Matteo Hessel
Tom Schaul
A. Guez
...
Gabriel Dulac-Arnold
David P. Reichert
Neil C. Rabinowitz
André Barreto
T. Degris
47
289
0
28 Dec 2016
Hierarchical Deep Reinforcement Learning: Integrating Temporal
  Abstraction and Intrinsic Motivation
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Tejas D. Kulkarni
Karthik Narasimhan
A. Saeedi
J. Tenenbaum
51
1,133
0
20 Apr 2016
Deep Reinforcement Learning with Double Q-learning
Deep Reinforcement Learning with Double Q-learning
H. V. Hasselt
A. Guez
David Silver
OffRL
131
7,590
0
22 Sep 2015
Continuous control with deep reinforcement learning
Continuous control with deep reinforcement learning
Timothy Lillicrap
Jonathan J. Hunt
Alexander Pritzel
N. Heess
Tom Erez
Yuval Tassa
David Silver
Daan Wierstra
193
13,174
0
09 Sep 2015
Illuminating search spaces by mapping elites
Illuminating search spaces by mapping elites
Jean-Baptiste Mouret
Jeff Clune
63
728
0
20 Apr 2015
Robots that can adapt like animals
Robots that can adapt like animals
Antoine Cully
Jeff Clune
Danesh Tarapore
Jean-Baptiste Mouret
61
1,032
0
13 Jul 2014
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