Learning from learners: Adapting reinforcement learning agents to be competitive in a card game
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 …
factors that contribute to our intelligence. Endowing artificial agents with this ability is not a …
Moody learners-explaining competitive behaviour of reinforcement learning agents
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 …
interactions is a challenging task. In a competitive scenario, the agent does not only have a …
Explicitly Maintaining Diverse Playing Styles in Self-Play
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 …
complex games through iteratively playing against an opponent from its historical versions …