2 citations · 3 across the 3 of their papers we have counts for
4 papers
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…
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…