Emergent Tool Use From Multi-Agent Autocurricula in Hide-and-Seek

This episode examines "Emergent Tool Use From Multi-Agent Autocurricula," in which hiders and seekers in a physics simulator develop strategies like building box forts, using ramps, and locking objects. All of this comes from a reward that only says whether a hider is visible. It sets the work against hand-written rewards and curiosity-driven baselines such as count-based exploration and Random Network Distillation. It also explains the setup: team-based self-play with PPO, movable boxes and ramps, and a head start for the hiders. The discussion asks whether the claimed six phases are measured or only watched. It looks at the statistics the authors track, such as box movement and locking, and notes that the paper mentions Elo and TrueSkill but doesn't run them. It also covers transfer tasks as a way to test what the agents learned. Listeners get a skeptical look at how self-play progress is measured, plus a clear account of why co-adapting agents make that hard.

Sources:

1. Emergent Tool Use From Multi-Agent Autocurricula in Hide-and-Seek

https://arxiv.org/pdf/1909.07528

2. Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research — Joel Z. Leibo, Edward Hughes, Marc Lanctot, Thore Graepel, 2019

https://scholar.google.com/scholar?q=Autocurricula+and+the+Emergence+of+Innovation+from+Social+Interaction%3A+A+Manifesto+for+Multi-Agent+Intelligence+Research

3. Emergent Complexity via Multi-Agent Competition — Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, Igor Mordatch, 2018

https://scholar.google.com/scholar?q=Emergent+Complexity+via+Multi-Agent+Competition

4. Grandmaster level in StarCraft II using multi-agent reinforcement learning — Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, et al. (DeepMind), 2019

https://scholar.google.com/scholar?q=Grandmaster+level+in+StarCraft+II+using+multi-agent+reinforcement+learning

5. Dota 2 with Large Scale Deep Reinforcement Learning — OpenAI: Christopher Berner, Greg Brockman, Brooke Chan, et al., 2019

https://scholar.google.com/scholar?q=Dota+2+with+Large+Scale+Deep+Reinforcement+Learning