Analysis of the possibilities for using machine learning algorithms in the Unity environment

A. Juliani, V. P. Berges, E. Vckay, Y. Gao, H. Henry, M. Mattar, D. Lange, Unity: A general platform for intelligent agents, arXiv preprint arXiv:1809.02627v2 (2020).
  Google Scholar

J. Schulman, F. Wolski, P. Dhariwal, A. Radford, O. Klimov, Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017).
  Google Scholar

T. Haarnoja, A. Zhou, P. Abbeel, S. Levine, Soft actorcritic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research, 80 (2018) 1861–1870.
  Google Scholar

J. Ho, S. Ermon, Generative adversarial imitation learning. Advances in neural information processing systems, (2016) 4565–4573.
  Google Scholar

A. Hussein, M. M. Gaber, E. Elyan, C. Jayne, Imitation Learning: A Survey of Learning Methods. ACM Computing Surveys (CSUR), 50(2) (2017) 1–35 https://doi.org/10.1145/3054912.
DOI: https://doi.org/10.1145/3054912   Google Scholar

R. S Sutton, A. G. Barto, Reinforcement Learning: An Introduction. Second edition. The MIT Press (2018).
  Google Scholar

J. Schulman, S. Levine, P. Abbeel, M. Jordan, P. Moritz, Trust region policy optimization. In International conference on machine learning (2015) 1889–1897.
  Google Scholar

M. Urmanov, M. Alimanova, A. Nurkey, Training Unity Machine Learning Agents using reinforcement learning method. In 2019 15th International Conference on Electronics, Computer and Computation (ICECCO), (2019) 1–4, https://doi.org/10.1109/ICECCO48375.2019.9043194.
DOI: https://doi.org/10.1109/ICECCO48375.2019.9043194   Google Scholar

M. Pleines, F. Zimmer, V. Berges, Action Spaces in Deep Reinforcement Learning to Mimic Human Input Devices, 2019 IEEE Conference on Games (CoG), (2019) 1–8 https://dx.doi.org/10.1109/CIG.2019.8848080.
DOI: https://doi.org/10.1109/CIG.2019.8848080   Google Scholar

V. Mnih, K. Kavukcuoglu, D. Silver et al., Human-level control through deep reinforcement learning. Nature, 518(7540) (2015) 529–533.
DOI: https://doi.org/10.1038/nature14236   Google Scholar

M. G. Bellemare, Y. Naddaf, J. Veness, M. Bowling, The arcade learning environment: An evaluation platform for general agents. Journal of Artificial Intelligence Research, 47 (2013) 253–279.
DOI: https://doi.org/10.1613/jair.3912   Google Scholar

A. P. Badia, B. Piot, S. Kapturowski, P. Sprechmann, A. Vitvitskyi, D. Guo, C. Blundell, Agent57: Outperforming the Atari Human Benchmark, International Conference on Machine Learning (2020) 507–517.
  Google Scholar

A. Defazio, T. Graepel, A comparison of learning algorithms on the arcade learning environment. arXiv preprint arXiv:1410.8620 (2014).
  Google Scholar

G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang,W. Zaremba, OpenAI Gym. arXiv preprint arXiv:1606.01540 (2016).
  Google Scholar

A. Tavakoli, F. Pardo, P. Kormushev, Action branching architectures for deep reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32. No. 1. (2018).
DOI: https://doi.org/10.1609/aaai.v32i1.11798   Google Scholar

Dokumentacja biblioteki ML-Agents Toolkit — opis i zalecany zakres wartości hiperparametrów uczenia, https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Training-Configuration-File.md, [04.05.2021].
  Google Scholar