Learning from learners: Adapting reinforcement learning agents to be competitive in a card game

P Barros, A Tanevska, A Sciutti - 2020 25th International …, 2021 - ieeexplore.ieee.org
Learning how to adapt to complex and dynamic environments is one of the most important
factors that contribute to our intelligence. Endowing artificial agents with this ability is not a …

Moody learners-explaining competitive behaviour of reinforcement learning agents

P Barros, A Tanevska, F Cruz… - 2020 Joint IEEE 10th …, 2020 - ieeexplore.ieee.org
Designing the decision-making processes of artificial agents that are involved in competitive
interactions is a challenging task. In a competitive scenario, the agent does not only have a …

Explicitly Maintaining Diverse Playing Styles in Self-Play

Y Liu, R Shen, M Li, Y Chen, J Zou, C Fan - openreview.net
Self-play has proven to be an effective training schema to obtain a high-level agent in
complex games through iteratively playing against an opponent from its historical versions …