424 citations · 860 across the 53 of their papers we have counts for
9 papers · 1 filter
Stabilizing Generative Adversarial Networks: A Survey
Maciej Wiatrak, Stefano V. Albrecht, Andrew Nystrom
Generative Adversarial Networks (GANs) are a type of generative model which have received much attention due to their ability to model complex real-world data. Despite their recent…
E-HBA: Using Action Policies for Expert Advice and Agent Typification
Stefano V. Albrecht, Jacob W. Crandall, Subramanian Ramamoorthy
Past research has studied two approaches to utilise predefined policy sets in repeated interactions: as experts, to dictate our own actions, and as types, to characterise the behav…
Comparative Evaluation of Multiagent Learning Algorithms in a Diverse Set of Ad Hoc Team Problems
Stefano V. Albrecht, Subramanian Ramamoorthy
This paper is concerned with evaluating different multiagent learning (MAL) algorithms in problems where individual agents may be heterogenous, in the sense of utilizing different…
On Convergence and Optimality of Best-Response Learning with Policy Types in Multiagent Systems
Stefano V. Albrecht, Subramanian Ramamoorthy
While many multiagent algorithms are designed for homogeneous systems (i.e. all agents are identical), there are important applications which require an agent to coordinate its act…
Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks (Extended Abstract)
Stefano V. Albrecht, Subramanian Ramamoorthy
Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.…
An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types
Stefano V. Albrecht, Jacob W. Crandall, Subramanian Ramamoorthy
Many multiagent applications require an agent to learn quickly how to interact with previously unknown other agents. To address this problem, researchers have studied learning algo…
Are You Doing What I Think You Are Doing? Criticising Uncertain Agent Models
Stefano V. Albrecht, S. Ramamoorthy
The key for effective interaction in many multiagent applications is to reason explicitly about the behaviour of other agents, in the form of a hypothesised behaviour. While there…
Reasoning about Hypothetical Agent Behaviours and their Parameters
Stefano V. Albrecht, Peter Stone
Agents can achieve effective interaction with previously unknown other agents by maintaining beliefs over a set of hypothetical behaviours, or types, that these agents may have. A…
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning
Georgios Papoudakis, Filippos Christianos, Arrasy Rahman +1
Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains. A particular approach which…