# Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agents

arXiv: https://arxiv.org/abs/2007.16045 · HTML: https://mlsanity.com/paper/2007.16045/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey](https://mlsanity.com/paper/2108.09003/) (2021)
- [Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications](https://mlsanity.com/paper/2207.01911/) (2022)
- [Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios](https://mlsanity.com/paper/2207.03214/) (2022)
- [Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenario](https://mlsanity.com/paper/2212.06967/) (2022)

## Abstract (arXiv)

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, do not show the temporal-relation between the selected actions. We address this problem by proposing the Moody framework. We evaluate our model by performing a series of experiments using the competitive multiplayer Chef's Hat card game and discuss how our model allows the agents' to obtain a holistic representation of the competitive dynamics within the game.