mlsanityData to 14 Oct 2026

Deep Learning Agents Trained For Avoidance Behave Like Hawks And Doves

arXiv 2503.11452 ↗First posted 14 Mar 2025cs.LGcited by 0

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Abstract (arXiv)

We present heuristically optimal strategies expressed by deep learning agents playing a simple avoidance game. We analyse the learning and behaviour of two agents within a symmetrical grid world that must cross paths to reach a target destination without crashing into each other or straying off of the grid world in the wrong direction. The agent policy is determined by one neural network that is employed in both agents. Our findings indicate that the fully trained network exhibits behaviour similar to that of the game Hawks and Doves, in that one agent employs an aggressive strategy to reach the target while the other learns how to avoid the aggressive agent.

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References · 17

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2015Prioritized Experience Replaycited by 1,099
  3. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
  4. 2017Robust Adversarial Reinforcement Learningcited by 451
  5. 2019Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learningcited by 93
  6. 2018Curriculum Adversarial Trainingcited by 89
  7. 2019Risk Averse Robust Adversarial Reinforcement Learningcited by 57
  8. 2021Breaking the Deadly Triad with a Target Networkcited by 47
  9. 2020Robust Reinforcement Learning via Adversarial training with Langevin Dynamicscited by 45
  10. 2024One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotioncited by 43
  11. 2023A Survey of Progress on Cooperative Multi-agent Reinforcement Learning in Open Environmentcited by 39
  12. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Surveycited by 32
5 more
  1. 2024Multi-Agent Dynamic Relational Reasoning for Social Robot Navigationcited by 20
  2. 2023Robust Adversarial Reinforcement Learning via Bounded Rationality Curriculacited by 10
  3. 2024ARMOR: Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planningcited by 4
  4. 2022A Reinforcement Learning Approach to Optimize Available Network Bandwidth Utilizationcited by 2
  5. 2023Multi Agent Navigation in Unconstrained Environments using a Centralized Attention based Graphical Neural Network Controllercited by 1

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