26 citations · 51 across the 4 of their papers we have counts for
5 papers · 1 filter
Persistent Message Passing
Heiko Strathmann, Mohammadamin Barekatain, Charles Blundell +1
Graph neural networks (GNNs) are a powerful inductive bias for modelling algorithmic reasoning procedures and data structures. Their prowess was mainly demonstrated on tasks featur…
Learning and Planning in Complex Action Spaces
Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou +3
Many important real-world problems have action spaces that are high-dimensional, continuous or both, making full enumeration of all possible actions infeasible. Instead, only small…
Online and Offline Reinforcement Learning by Planning with a Learned Model
Julian Schrittwieser, Thomas Hubert, Amol Mandhane +3
Learning efficiently from small amounts of data has long been the focus of model-based reinforcement learning, both for the online case when interacting with the environment and th…
MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics
Mohammadamin Barekatain, Ryo Yonetani, Masashi Hamaya
Transfer reinforcement learning (RL) aims at improving the learning efficiency of an agent by exploiting knowledge from other source agents trained on relevant tasks. However, it r…
Machine learning for Internet of Things data analysis: A survey
Mohammad Saeid Mahdavinejad, Mohammadreza Rezvan, Mohammadamin Barekatain +3
Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurem…