Papers › Adaptive Transformers in RL

Adaptive Transformers in RL

8 Apr 2020arXiv:2004.03761archive 2025-07-28

Shakti Kumar, Jerrod Parker, Panteha Naderian

Recent developments in Transformers have opened new interesting areas of research in partially observable reinforcement learning tasks. Results from late 2019 showed that Transformers are able to outperform LSTMs on both memory intense and reactive tasks. In this work we first partially replicate the results shown in Stabilizing Transformers in RL on both reactive and memory based environments. We then show performance improvement coupled with reduced computation when adding adaptive attention span to this Stable Transformer on a challenging DMLab30 environment. The code for all our experiments and models is available at https://github.com/jerrodparker20/adaptive-transformers-in-rl.

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Partially Observable Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer ReLU Residual Connection Softmax Transformer

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