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abstract: '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 dynamic environment but also is directly affected by the opponents’ actions. Observing the Q-values of the agent is usually a way of explaining its behavior, however, it does not show the temporal-relation between the selected actions. We address this problem by proposing the Moody framework that creates an intrinsic representation for each agent based on the Pleasure/Arousal model. We evaluate our model by performing a series of experiments using the competitive multiplayer Chef’s Hat card game and discuss how by observing the intrinsic state generated by our model allows us to obtain a holistic representation of the competitive dynamics within the game.'
author:
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bibliography:
- 'bib.bib'
title: 'Moody Learners - Explaining Competitive Behaviour of Reinforcement Learning Agents'
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Explainable Artificial Intelligence, Reinforcement Learning, Intrinsic Confidence.

Introduction
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The application of reinforcement learning (RL) models has been on a rise following the onset of deep reinforcement learning [@mnih2013playing]. The ability of RL models to solve complex problems, represented mostly by the association of high-dimensional states and a large number of 