Colloquium Archives for Academic Year 2020

Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning.

When: Wed, October 21, 2020 - 1:30pm
Speaker: Jayesh Gupta (Stanford and Microsoft Research) -
Abstract: Multi-agent reinforcement learning (MARL) requires coordination to efficiently solve certain tasks. Fully centralized control is often infeasible in such domains due to the size of joint action spaces. Coordination graph based formalization allows reasoning about the joint action based on the structure of interactions. However, they often require domain expertise in their design. In this talk, we will discuss the recently introduced deep implicit coordination graph (DICG) architecture for such scenarios. DICG consists of a module for inferring the dynamic coordination graph structure which is then used by a graph neural network based module to learn to implicitly reason about the joint actions or values. DICG allows learning the tradeoff between full centralization and decentralization via standard actor-critic methods to significantly improve coordination for domains with large numbers of agents.


When: Wed, December 2, 2020 - 3:15pm
Where: Kirwan Hall 3206
Speaker: Joergen Andersen (Danish Institute for Advanced Study) -