# Deep Learning Agents Trained For Avoidance Behave Like Hawks And Doves

arXiv: https://arxiv.org/abs/2503.11452 · HTML: https://mlsanity.com/paper/2503.11452/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## Builds on (most cited first, 17 of 17)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1906.04737/) (2019)
- [Curriculum Adversarial Training](https://mlsanity.com/paper/1805.04807/) (2018)
- [Risk Averse Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1904.00511/) (2019)
- [Breaking the Deadly Triad with a Target Network](https://mlsanity.com/paper/2101.08862/) (2021)
- [Robust Reinforcement Learning via Adversarial training with Langevin Dynamics](https://mlsanity.com/paper/2002.06063/) (2020)
- [One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion](https://mlsanity.com/paper/2409.06366/) (2024)
- [A Survey of Progress on Cooperative Multi-agent Reinforcement Learning in Open Environment](https://mlsanity.com/paper/2312.01058/) (2023)
- [Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.09675/) (2024)
- [Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation](https://mlsanity.com/paper/2401.12275/) (2024)
- [Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula](https://mlsanity.com/paper/2311.01642/) (2023)
- [ARMOR: Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planning](https://mlsanity.com/paper/2412.00396/) (2024)
- [A Reinforcement Learning Approach to Optimize Available Network Bandwidth Utilization](https://mlsanity.com/paper/2211.11949/) (2022)
- [Multi Agent Navigation in Unconstrained Environments using a Centralized Attention based Graphical Neural Network Controller](https://mlsanity.com/paper/2307.16727/) (2023)

## 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.