Janos Kramar
Janos Kramar
Verified email at google.com
Cited by
Cited by
Zoneout: Regularizing rnns by randomly preserving hidden activations
D Krueger, T Maharaj, J Kramár, M Pezeshki, N Ballas, NR Ke, A Goyal, ...
arXiv preprint arXiv:1606.01305, 2016
Reinforcement and imitation learning for diverse visuomotor skills
Y Zhu, Z Wang, J Merel, A Rusu, T Erez, S Cabi, S Tunyasuvunakool, ...
arXiv preprint arXiv:1802.09564, 2018
OpenSpiel: A framework for reinforcement learning in games
M Lanctot, E Lockhart, JB Lespiau, V Zambaldi, S Upadhyay, J Pérolat, ...
arXiv preprint arXiv:1908.09453, 2019
Guidelines for artificial intelligence containment
J Babcock, J Kramár, RV Yampolskiy
Next-Generation Ethics: Engineering a Better Society, 90-112, 2019
The AGI containment problem
J Babcock, J Kramár, R Yampolskiy
International Conference on Artificial General Intelligence, 53-63, 2016
Learning reciprocity in complex sequential social dilemmas
T Eccles, E Hughes, J Kramár, S Wheelwright, JZ Leibo
arXiv preprint arXiv:1903.08082, 2019
Learning to play no-press diplomacy with best response policy iteration
T Anthony, T Eccles, A Tacchetti, J Kramár, I Gemp, TC Hudson, N Porcel, ...
arXiv preprint arXiv:2006.04635, 2020
OpenSpiel: A Framework for Reinforcement Learning in Games. CoRR abs/1908.09453 (2019)
M Lanctot, E Lockhart, JB Lespiau, V Zambaldi, S Upadhyay, J Pérolat, ...
arXiv preprint cs.LG/1908.09453, 2019
The Imitation Game: Learned Reciprocity in Markov games.
T Eccles, E Hughes, J Kramár, S Wheelwright, JZ Leibo
AAMAS, 1934-1936, 2019
Reinforcement and imitation learning for a task
S Tunyasuvunakool, Y Zhu, J Merel, J Kramar, Z Wang, NMO Heess
US Patent App. 16/174,112, 2019
A generalized-zero-preserving method for compact encoding of concept lattices
M Skala, V Krakovna, J Kramár, G Penn
Proceedings of the 48th annual meeting of the Association for Computational …, 2010
How intelligible is intelligence
A Salamon, S Rayhawk, J Kramár
Proceedings of the VIII European conference on computing and philosophy …, 2010
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent
I Gemp, R Savani, M Lanctot, Y Bachrach, T Anthony, R Everett, ...
arXiv preprint arXiv:2106.01285, 2021
Should I tear down this wall? Optimizing social metrics by evaluating novel actions
J Kramár, N Rabinowitz, T Eccles, A Tacchetti
arXiv preprint arXiv:2004.07625, 2020
Neural Design of Contests and All-Pay Auctions using Multi-Agent Simulation
T Anthony, I Gemp, J Kramar, T Eccles, A Tacchetti, Y Bachrach
A Neural Network Auction For Group Decision Making Over a Continuous Space
Y Bachrach, I Gemp, M Garnelo, J Kramar, T Eccles, D Rosenbaum, ...
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