1 code implementation • 30 Nov 2018 • Aleksandra Malysheva, Tegg Taekyong Sung, Chae-Bong Sohn, Daniel Kudenko, Aleksei Shpilman
Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • 7 Feb 2019 • Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty, Jennifer Hicks, Sean F. Carroll, Bo Zhou, Hongsheng Zeng, Fan Wang, Rongzhong Lian, Hao Tian, Wojciech Jaśkowski, Garrett Andersen, Odd Rune Lykkebø, Nihat Engin Toklu, Pranav Shyam, Rupesh Kumar Srivastava, Sergey Kolesnikov, Oleksii Hrinchuk, Anton Pechenko, Mattias Ljungström, Zhen Wang, Xu Hu, Zehong Hu, Minghui Qiu, Jun Huang, Aleksei Shpilman, Ivan Sosin, Oleg Svidchenko, Aleksandra Malysheva, Daniel Kudenko, Lance Rane, Aditya Bhatt, Zhengfei Wang, Penghui Qi, Zeyang Yu, Peng Peng, Quan Yuan, Wenxin Li, Yunsheng Tian, Ruihan Yang, Pingchuan Ma, Shauharda Khadka, Somdeb Majumdar, Zach Dwiel, Yinyin Liu, Evren Tumer, Jeremy Watson, Marcel Salathé, Sergey Levine, Scott Delp
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
no code implementations • 13 Mar 2019 • Kleanthis Malialis, Sam Devlin, Daniel Kudenko
These are learning time, scalability and decentralised coordination i. e. no communication between the learning agents.
no code implementations • 13 Nov 2019 • Nourah ALRossais, Daniel Kudenko
In the context of stereotypes creation for recommender systems, we found that certain types of categorical variables pose particular challenges if simple clustering procedures were employed with the objective to create stereotypes.
no code implementations • 6 Apr 2020 • John Burden, Daniel Kudenko
Potential Based Reward Shaping combined with a potential function based on appropriately defined abstract knowledge has been shown to significantly improve learning speed in Reinforcement Learning.
1 code implementation • 29 Apr 2020 • Vikram Waradpande, Daniel Kudenko, Megha Khosla
Motivated by the recent success of node representations for several graph analytical tasks we specifically investigate the capability of node representation learning methods to effectively encode the topology of the underlying MDP in Deep RL.
no code implementations • 2 Aug 2020 • Andrea Bassich, Francesco Foglino, Matteo Leonetti, Daniel Kudenko
Curriculum Learning for Reinforcement Learning is an increasingly popular technique that involves training an agent on a sequence of intermediate tasks, called a Curriculum, to increase the agent's performance and learning speed.
no code implementations • 16 Dec 2020 • Aleksei Shpilman, Dmitry Boikiy, Marina Polyakova, Daniel Kudenko, Anton Burakov, Elena Nadezhdina
Human experts find it particularly difficult to recognize the levels of chemical compound exposure of a cell.
no code implementations • 16 Dec 2020 • Anastasia Gaydashenko, Daniel Kudenko, Aleksei Shpilman
Robot navigation through crowds poses a difficult challenge to AI systems, since the methods should result in fast and efficient movement but at the same time are not allowed to compromise safety.
no code implementations • 16 Dec 2020 • Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman
In this paper, we demonstrate how data from videos of human running (e. g. taken from YouTube) can be used to shape the reward of the humanoid learning agent to speed up the learning and produce a better result.
no code implementations • 16 Dec 2020 • Ivan Sosin, Daniel Kudenko, Aleksei Shpilman
Movement control of artificial limbs has made big advances in recent years.
no code implementations • 17 Dec 2020 • Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman
Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 19 May 2022 • Amir Abolfazli, Gregory Palmer, Daniel Kudenko
The success of deep reinforcement learning (DRL) hinges on the availability of training data, which is typically obtained via a large number of environment interactions.
no code implementations • 19 Apr 2024 • Dren Fazlija, Arkadij Orlov, Johanna Schrader, Monty-Maximilian Zühlke, Michael Rohs, Daniel Kudenko
However, surveying existing image-based methods, we noticed a need for more human evaluations of the proposed image modifications.