5 research outputs found
Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games
Many real-world multi-agent interactions consider multiple distinct criteria,
i.e. the payoffs are multi-objective in nature. However, the same
multi-objective payoff vector may lead to different utilities for each
participant. Therefore, it is essential for an agent to learn about the
behaviour of other agents in the system. In this work, we present the first
study of the effects of such opponent modelling on multi-objective multi-agent
interactions with non-linear utilities. Specifically, we consider two-player
multi-objective normal form games with non-linear utility functions under the
scalarised expected returns optimisation criterion. We contribute novel
actor-critic and policy gradient formulations to allow reinforcement learning
of mixed strategies in this setting, along with extensions that incorporate
opponent policy reconstruction and learning with opponent learning awareness
(i.e., learning while considering the impact of one's policy when anticipating
the opponent's learning step). Empirical results in five different MONFGs
demonstrate that opponent learning awareness and modelling can drastically
alter the learning dynamics in this setting. When equilibria are present,
opponent modelling can confer significant benefits on agents that implement it.
When there are no Nash equilibria, opponent learning awareness and modelling
allows agents to still converge to meaningful solutions that approximate
equilibria.Comment: Under review since 14 November 202
Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning
Contains fulltext :
228326pre.pdf (preprint version ) (Open Access)
Contains fulltext :
228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202