Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agents
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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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Jul 2020.
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Citing papers per year: 2021: 1; 2022: 3.
4 papers.
- 2021Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Surveycited by 24
- 2022Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applicationscited by 6
- 2022Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarioscited by 5
- 2022Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenariocited by 0
- 2022Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenario14 Dec 2022
- 2022Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios7 Jul 2022
- 2022Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications5 Jul 2022
- 2021Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey20 Aug 2021