153 citations · 214 across the 6 of their papers we have counts for
6 papers
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
Julien Perolat, Bart de Vylder, Daniel Hennes +31
We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level. Stratego is one of the few…
End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box
Vladislav Belyaev, Aleksandra Malysheva, Aleksei Shpilman
The task object tracking is vital in numerous applications such as autonomous driving, intelligent surveillance, robotics, etc. This task entails the assigning of a bounding box to…
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning
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 domain…
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data
Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman
Learning to produce efficient movement behaviour for humanoid robots from scratch is a hard problem, as has been illustrated by the "Learning to run" competition at NIPS 2017. The…
Artificial Intelligence for Prosthetics - challenge solutions
Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty +47
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 giv…
Deep Multi-Agent Reinforcement Learning with Relevance Graphs
Aleksandra Malysheva, Tegg Taekyong Sung, Chae-Bong Sohn +2
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 domain…