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Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review
Amara Zuffer, Michael Burke, Mehrtash Harandi
The diversity of tasks and dynamic nature of reinforcement learning (RL) require RL agents to be able to learn sequentially and continuously, a learning paradigm known as continuou…
Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations
Bryce Ferenczi, Michael Burke, Tom Drummond
Various works have aimed at combining the inference efficiency of recurrent models and training parallelism of multi-head attention for sequence modeling. However, most of these wo…
Carefully Structured Compression: Efficiently Managing StarCraft II Data
Bryce Ferenczi, Rhys Newbury, Michael Burke +1
Creation and storage of datasets are often overlooked input costs in machine learning, as many datasets are simple image label pairs or plain text. However, datasets with more comp…