5 citations · 5 across the 3 of their papers we have counts for
4 papers · 1 filter
A Clean Slate for Offline Reinforcement Learning
Matthew Thomas Jackson, Uljad Berdica, Jarek Liesen +2
Progress in offline reinforcement learning (RL) has been impeded by ambiguous problem definitions and entangled algorithmic designs, resulting in inconsistent implementations, insu…
Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps
Benjamin Ellis, Matthew T. Jackson, Andrei Lupu +4
In reinforcement learning (RL), it is common to apply techniques used broadly in machine learning such as neural network function approximators and momentum-based optimizers. Howev…
Hypernetworks in Meta-Reinforcement Learning
Jacob Beck, Matthew Thomas Jackson, Risto Vuorio +1
Training a reinforcement learning (RL) agent on a real-world robotics task remains generally impractical due to sample inefficiency. Multi-task RL and meta-RL aim to improve sample…
Multi-Modal Fusion by Meta-Initialization
Matthew T. Jackson, Shreshth A. Malik, Michael T. Matthews +1
When experience is scarce, models may have insufficient information to adapt to a new task. In this case, auxiliary information - such as a textual description of the task - can en…