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In this project we demonstrate the effectiveness of the transformer encoderas a viable architecture for policies in variable action environments. Usingit, we train an agent using Proximal Policy Optimisation (PPO) on multiple mapsagainst scripted opponents in the Gym-$\mu$RTS environment. The final agent isable to achieve a higher return using half the computational resources of thenext-best RL agent, which used the GridNet architecture.  The source code and pre-trained models are available here:https://github.com/NiklasZ/transformers-for-variable-action-envs

Transformers as Policies for Variable Action Environments