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 simple task, particularly in competitive
scenarios. In this paper, we present a broad study on how popular reinforcement
learning algorithms can be adapted and implemented to learn and to play a
real-world implementation of a competitive multiplayer card game. We propose
specific training and validation routines for the learning agents, in order to
evaluate how the agents learn to be competitive and explain how they adapt to
each others' playing style. Finally, we pinpoint how the behavior of each agent
derives from their learning style and create a baseline for future research on
this scenario.
