Deep Reinforcement Learning with Transformers for Text Adventure Games
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- IEEE Conference on Computatonal Intelligence and Games, CIG, 2020, 2020-August, pp. 65-72
- Issue Date:
- 2020-08-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 09231622.pdf | Published version | 3.47 MB |
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In this paper, we study transformers for text-based games. As a promising replacement of recurrent modules in Natural Language Processing (NLP) tasks, the transformer architecture could be treated as a powerful state representation generator for reinforcement learning. However, the vanilla transformer is neither effective nor efficient to learn with a huge amount of weight parameters. Unlike existing research that encodes states using LSTMs or GRUs, we develop a novel lightweight transformer-based representation generator featured with reordered layer normalization, weight sharing and block-wise aggregation. The experimental results show that our proposed model not only solves single games with much fewer interactions, but also achieves better generalization on a set of unseen games. Furthermore, our model outperforms state-of-the-art agents in a variety of man-made games.
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