# Incorporating Rivalry in Reinforcement Learning for a Competitive Game

arXiv: https://arxiv.org/abs/2208.10327 · HTML: https://mlsanity.com/paper/2208.10327/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 66 references that are in the map.
- First posted on arXiv 22 Aug 2022.

## Builds on (most cited first, 16 of 16)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Explainable Reinforcement Learning Through a Causal Lens](https://mlsanity.com/paper/1905.10958/) (2019)
- [Emergent Complexity via Multi-Agent Competition](https://mlsanity.com/paper/1710.03748/) (2017)
- [A Survey of Deep Reinforcement Learning in Video Games](https://mlsanity.com/paper/1912.10944/) (2019)
- [A multi-agent reinforcement learning model of common-pool resource appropriation](https://mlsanity.com/paper/1707.06600/) (2017)
- [Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and Limitations](https://mlsanity.com/paper/1912.09007/) (2019)
- [A Survey on Interactive Reinforcement Learning: Design Principles and Open Challenges](https://mlsanity.com/paper/2105.12949/) (2021)
- [Emotion in Reinforcement Learning Agents and Robots: A Survey](https://mlsanity.com/paper/1705.05172/) (2017)
- [On the Utility of Model Learning in HRI](https://mlsanity.com/paper/1901.01291/) (2019)
- [Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments](https://mlsanity.com/paper/1804.00361/) (2018)
- [Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario](https://mlsanity.com/paper/2006.13615/) (2020)
- [Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning](https://mlsanity.com/paper/2003.05012/) (2020)
- [Ensemble-Based Deep Reinforcement Learning for Chatbots](https://mlsanity.com/paper/1908.10422/) (2019)
- [Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card Game](https://mlsanity.com/paper/2004.04000/) (2020)
- [It's Food Fight! Introducing the Chef's Hat Card Game for Affective-Aware HRI](https://mlsanity.com/paper/2002.11458/) (2020)

## Abstract (arXiv)

Recent advances in reinforcement learning with social agents have allowed such models to achieve human-level performance on specific interaction tasks. However, most interactive scenarios do not have a version alone as an end goal; instead, the social impact of these agents when interacting with humans is as important and largely unexplored. In this regard, this work proposes a novel reinforcement learning mechanism based on the social impact of rivalry behavior. Our proposed model aggregates objective and social perception mechanisms to derive a rivalry score that is used to modulate the learning of artificial agents. To investigate our proposed model, we design an interactive game scenario, using the Chef's Hat Card Game, and examine how the rivalry modulation changes the agent's playing style, and how this impacts the experience of human players in the game. Our results show that humans can detect specific social characteristics when playing against rival agents when compared to common agents, which directly affects the performance of the human players in subsequent games. We conclude our work by discussing how the different social and objective features that compose the artificial rivalry score contribute to our results.