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1906.09480
Cited By
A neurally plausible model learns successor representations in partially observable environments
Neural Information Processing Systems (NeurIPS), 2019
22 June 2019
Eszter Vértes
M. Sahani
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Papers citing
"A neurally plausible model learns successor representations in partially observable environments"
15 / 15 papers shown
A Distributional Analogue to the Successor Representation
Harley Wiltzer
Jesse Farebrother
Arthur Gretton
Yunhao Tang
André Barreto
Will Dabney
Marc G. Bellemare
Mark Rowland
447
11
0
13 Feb 2024
Distributional Bellman Operators over Mean Embeddings
International Conference on Machine Learning (ICML), 2023
Wenliang Kevin Li
Grégoire Delétang
Matthew Aitchison
Marcus Hutter
Anian Ruoss
Arthur Gretton
Mark Rowland
OffRL
301
5
0
09 Dec 2023
An introduction to reinforcement learning for neuroscience
Neurons, Behavior, Data analysis, and Theory (NBDT), 2023
Kristopher T. Jensen
OOD
OffRL
AI4CE
197
9
0
13 Nov 2023
Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning
Parvin Malekzadeh
Ming Hou
Konstantinos N. Plataniotis
357
3
0
16 Oct 2023
A Rubric for Human-like Agents and NeuroAI
Philosophical Transactions of the Royal Society of London. Biological Sciences (Phil. Trans. R. Soc. B), 2022
Ida Momennejad
294
20
0
08 Dec 2022
Unsupervised representation learning with recognition-parametrised probabilistic models
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
William I. Walker
Hugo Soulat
Changmin Yu
M. Sahani
BDL
304
6
0
13 Sep 2022
AKF-SR: Adaptive Kalman Filtering-based Successor Representation
Parvin Malekzadeh
Mohammad Salimibeni
Ming Hou
Arash Mohammadi
Konstantinos N. Plataniotis
297
6
0
31 Mar 2022
Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation
Italian National Conference on Sensors (INS), 2021
Mohammad Salimibeni
Arash Mohammadi
Parvin Malekzadeh
Konstantinos N. Plataniotis
217
6
0
30 Dec 2021
Tuning the Weights: The Impact of Initial Matrix Configurations on Successor Features Learning Efficacy
Hyunsu Lee
235
5
0
03 Nov 2021
Successor Feature Representations
Chris Reinke
Xavier Alameda-Pineda
363
6
0
29 Oct 2021
A First-Occupancy Representation for Reinforcement Learning
Theodore H. Moskovitz
S. Wilson
M. Sahani
384
18
0
28 Sep 2021
Successor Feature Sets: Generalizing Successor Representations Across Policies
AAAI Conference on Artificial Intelligence (AAAI), 2021
Kianté Brantley
Soroush Mehri
Geoffrey J. Gordon
OffRL
273
11
0
03 Mar 2021
A learning perspective on the emergence of abstractions: the curious case of phonemes
Language and Cognition (LC), 2020
P. Milin
Benjamin V. Tucker
Dagmar Divjak
241
8
0
14 Dec 2020
Biological credit assignment through dynamic inversion of feedforward networks
Neural Information Processing Systems (NeurIPS), 2020
William F. Podlaski
C. Machens
291
22
0
10 Jul 2020
Deep Reinforcement Learning and its Neuroscientific Implications
Neuron (Neuron), 2020
M. Botvinick
Jane X. Wang
Will Dabney
Kevin J. Miller
Z. Kurth-Nelson
OffRL
AI4CE
190
213
0
07 Jul 2020
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