Wojciech Jaśkowski
Wojciech Jaśkowski
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Cited by
Cited by
Vizdoom: A doom-based ai research platform for visual reinforcement learning
M Kempka, M Wydmuch, G Runc, J Toczek, W Jaśkowski
2016 IEEE conference on computational intelligence and games (CIG), 1-8, 2016
Genetic programming needs better benchmarks
J McDermott, DR White, S Luke, L Manzoni, M Castelli, L Vanneschi, ...
Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012
Better GP benchmarks: community survey results and proposals
DR White, J McDermott, M Castelli, L Manzoni, BW Goldman, ...
Genetic Programming and Evolvable Machines 14 (1), 3-29, 2013
Model-based Active Exploration
P Shyam, W Jaśkowski, F Gomez
arXiv preprint arXiv:1810.12162, 2018
ViZDoom Competitions: Playing Doom From Pixels
M Wydmuch, M Kempka, W Jaśkowski
IEEE Transactions on Games 11 (3), 248-259, 2018
Temporal difference learning of n-tuple networks for the game 2048
M Szubert, W Jaśkowski
2014 IEEE Conference on Computational Intelligence and Games, 1-8, 2014
Training agents using upside-down reinforcement learning
RK Srivastava, P Shyam, F Mutz, W Jaśkowski, J Schmidhuber
arXiv preprint arXiv:1912.02877, 2019
The influence of motor imagery on the learning of a fine hand motor skill
J Sobierajewicz, A Przekoracka-Krawczyk, W Jaśkowski, WB Verwey, ...
Experimental brain research 235 (1), 305-320, 2017
Coevolutionary temporal difference learning for Othello
M Szubert, W Jaskowski, K Krawiec
Computational Intelligence and Games, 2009. CIG 2009. IEEE Symposium on, 104-111, 2009
Learning to Play Othello with Deep Neural Networks
P Liskowski, W Jaśkowski, K Krawiec
IEEE Transactions on Games 10 (4), 354-364, 2018
Artificial intelligence for prosthetics: Challenge solutions
Ł Kidziński, C Ong, SP Mohanty, J Hicks, S Carroll, B Zhou, H Zeng, ...
The NeurIPS'18 Competition, 69-128, 2020
Mastering 2048 with delayed temporal coherence learning, multistage weight promotion, redundant encoding, and carousel shaping
W Jaśkowski
IEEE Transactions on Games 10 (1), 3-14, 2017
On scalability, generalization, and hybridization of coevolutionary learning: a case study for othello
M Szubert, W Jaśkowski, K Krawiec
IEEE Transactions on Computational Intelligence and AI in Games 5 (3), 214-226, 2013
Heterogeneous team deep Q-learning in low-dimensional multi-agent environments
M Kurek, W Jaśkowski
2016 IEEE Conference on Computational Intelligence and Games (CIG), 1-8, 2016
Winning ant wars: Evolving a human-competitive game strategy using fitnessless selection
W Jaśkowski, K Krawiec, B Wieloch
Genetic Programming, 13-24, 2008
Improving coevolution by random sampling
W Jaśkowski, P Liskowski, M Szubert, K Krawiec
Proceedings of the 15th annual conference on Genetic and evolutionary …, 2013
Evolving strategy for a probabilistic game of imperfect information using genetic programming
W Jaśkowski, K Krawiec, B Wieloch
Genetic Programming and Evolvable Machines 9 (4), 281-294, 2008
Reinforcement Learning to Run… Fast
W Jaśkowski, OR Lykkebř, NE Toklu, F Trifterer, Z Buk, J Koutník, ...
The NIPS'17 Competition: Building Intelligent Systems, 155-167, 2018
To what extent can motor imagery replace motor execution while learning a fine motor skill?
J Sobierajewicz, S Szarkiewicz, A Przekoracka-Krawczyk, W Jaśkowski, ...
Advances in cognitive psychology 12 (4), 179, 2016
Formal analysis, hardness, and algorithms for extracting internal structure of test-based problems
W Jaśkowski, K Krawiec
Evolutionary computation 19 (4), 639-671, 2011
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