7 citations · 16 across the 7 of their papers we have counts for
7 papers
Wasserstein Policy Optimization
David Pfau, Ian Davies, Diana Borsa +3
We introduce Wasserstein Policy Optimization (WPO), an actor-critic algorithm for reinforcement learning in continuous action spaces. WPO can be derived as an approximation to Wass…
Towards practical reinforcement learning for tokamak magnetic control
Brendan D. Tracey, Andrea Michi, Yuri Chervonyi +15
Reinforcement learning (RL) has shown promising results for real-time control systems, including the domain of plasma magnetic control. However, there are still significant drawbac…
One Step at a Time: Pros and Cons of Multi-Step Meta-Gradient Reinforcement Learning
Clément Bonnet, Paul Caron, Thomas Barrett +2
Self-tuning algorithms that adapt the learning process online encourage more effective and robust learning. Among all the methods available, meta-gradients have emerged as a promis…
Fully Distributed Actor-Critic Architecture for Multitask Deep Reinforcement Learning
Sergio Valcarcel Macua, Ian Davies, Aleksi Tukiainen +1
We propose a fully distributed actor-critic architecture, named Diff-DAC, with application to multitask reinforcement learning (MRL). During the learning process, agents communicat…
Kernel Identification Through Transformers
Fergus Simpson, Ian Davies, Vidhi Lalchand +3
Kernel selection plays a central role in determining the performance of Gaussian Process (GP) models, as the chosen kernel determines both the inductive biases and prior support of…
Learning to Model Opponent Learning
Ian Davies, Zheng Tian, Jun Wang
Multi-Agent Reinforcement Learning (MARL) considers settings in which a set of coexisting agents interact with one another and their environment. The adaptation and learning of oth…
Learning to Communicate Implicitly By Actions
Zheng Tian, Shihao Zou, Ian Davies +4
In situations where explicit communication is limited, human collaborators act by learning to: (i) infer meaning behind their partner's actions, and (ii) convey private information…