Deep imitation learning for 3D navigation tasks.
Gaber, Mohamed Medhat
MetadataShow full item record
HUSSEIN, A., ELYAN, E., GABER, M.M. and JAYNE, C. 2018. Deep imitation learning for 3D navigation tasks. Neural computing and applications [online], 29(7), pages 389-404. Available from: https://doi.org/10.1007/s00521-017-3241-z
Deep learning techniques have shown success in learning from raw high dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imitation learning has been scarcely explored. Imitation learning can be an efficient method to teach intelligent agents by providing a set of demonstrations to learn from. However, generalizing to situations that are not represented in the demonstrations can be challenging, especially in 3D environments. In this paper, we propose a deep imitation learning method to learn navigation tasks from demonstrations in a 3D environment. The supervised policy is refined using active learning in order to generalize to unseen situations. This approach is compared to two popular deep reinforcement learning techniques: Deep-Q-networks (DQN) and Asynchronous actor critic (A3C). The proposed method as well as the reinforcement learning methods employ deep convolutional neural networks and learn directly from raw visual input. Methods for combining learning from demonstrations and experience are also investigated. This combination aims to join the generalization ability of learning by experience with the efficiency of learning by imitation. The proposed methods are evaluated on 4 navigation tasks in a 3D simulated environment. Navigation tasks are a typical problem that is relevant to many real applications. They pose the challenge of requiring demonstrations of long trajectories to reach the target and only providing delayed rewards (usually terminal) to the agent. The experiments show that the proposed method can successfully learn navigation tasks from raw visual input while learning from experience methods fail to learn an effiective policy. Moreover, it is shown that active learning can significantly improve the performance of the initially learned policy using a small number of active samples.
Permalink for this recordhttp://hdl.handle.net/10059/2543
Collections in which this item appears
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0
Showing items related by title, author, creator and subject.
Fulford, Heather; Bailey, Moira (Academic Conferences and Publishing International Limited http://www.proceedings.com/24280.html, 2014-09-18)FULFORD, H. and BAILEY, M. 2014. Journals and jottings on entrepreneurial learning journeys. In Galbraith, B. (ed.) Proceedings of the 9th European conference on innovation and entrepreneurship (ECIE 2014), 18-19 September 2014, Belfast, UK. Reading: Academic Conferences Ltd [online], pages 198-206. Available from: http://www.proceedings.com/24280.htmlReview of relevant literature highlighted that entrepreneurs need help to reflect on, and make sense of, the challenges and opportunities that occur during the entrepreneurial process. For students who are unfamiliar with ...
Hussein, Ahmed; Gaber, Mohamed Medhat; Elyan, Eyad; Jayne, Chrisina (ACM https://doi.org/10.1145/3054912, 2017-04-11)HUSSEIN, A., GABER, M.M., ELYAN, E. and JAYNE, C. 2017 Imitation learning: a survey of learning methods. ACM computing surveys [online], 50(2), article 21. Available from: https://doi.org/10.1145/3054912Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of ...
Martin, Kyle; Wiratunga, Nirmalie; Massie, Stewart; Clos, Jérémie (Springer https://doi.org/10.1007/978-3-030-04191-5_3, 2018-11-16)MARTIN, K., WIRATUNGA, N., MASSIE, S. and CLOS, J. 2018. Informed pair selection for self-paced metric learning in Siamese neural networks. In Bramer, M. and PETRIDIS, M. (eds.) Artificial intelligence xxxv: proceedings of the 38th British Computer Society's specialist group on artificial intelligence (SGAI) annual international artificial intelligence conference (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in artificial intelligence, 11311. Cham: Springer [online], pages 34-49. Available from: https://doi.org/10.1007/978-3-030-04191-5_3Siamese Neural Networks (SNNs) are deep metric learners that use paired instance comparisons to learn similarity. The neural feature maps learnt in this way provide useful representations for classification tasks. Learning ...