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Transformers as Policies for Variable Action Environments

2023-01-09 21:03:10
Niklas Zwingenberger
arXiv_AI
       
Abstract

In this project we demonstrate the effectiveness of the transformer encoder as a viable architecture for policies in variable action environments. Using it, we train an agent using Proximal Policy Optimisation (PPO) on multiple maps against scripted opponents in the Gym-$\mu$RTS environment. The final agent is able to achieve a higher return using half the computational resources of the next-best RL agent, which used the GridNet architecture. The source code and pre-trained models are available here: this https URL

Abstract (translated)

URL

https://arxiv.org/abs/2301.03679

PDF

https://arxiv.org/pdf/2301.03679.pdf

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