Transformers as Policies for Variable Action Environments
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Abstract (arXiv)
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-μRTS 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: https://github.com/NiklasZ/transformers-for-variable-action-envs
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 16 references that are in the map. First posted on arXiv 9 Jan 2023.
Builds on
12 of 16 references.
References · 12
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2015Trust Region Policy Optimizationcited by 2,503
- 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,219
- 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
- 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,528
- 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,327
- 2020Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPOcited by 184
- 2020A Closer Look at Invalid Action Masking in Policy Gradient Algorithmscited by 160
- 2020What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Studycited by 157
- 2021Gym-μRTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learningcited by 19
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1 paper.