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Incorporating Rivalry in Reinforcement Learning for a Competitive Game

arXiv 2208.10327 ↗First posted 22 Aug 2022cs.AIcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 66 references that are in the map. First posted on arXiv 22 Aug 2022.

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16 of 66 references.

References · 16

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
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  10. 2019On the Utility of Model Learning in HRIcited by 21
  11. 2018Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environmentscited by 19
  12. 2020Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenariocited by 19
4 more
  1. 2020Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learningcited by 18
  2. 2019Ensemble-Based Deep Reinforcement Learning for Chatbotscited by 11
  3. 2020Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card Gamecited by 3
  4. 2020It's Food Fight! Introducing the Chef's Hat Card Game for Affective-Aware HRIcited by 2

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