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Recent developments in Transformers have opened new interesting areas ofresearch in partially observable reinforcement learning tasks. Results fromlate 2019 showed that Transformers are able to outperform LSTMs on both memoryintense and reactive tasks. In this work we first partially replicate theresults shown in Stabilizing Transformers in RL on both reactive and memorybased environments. We then show performance improvement coupled with reducedcomputation when adding adaptive attention span to this Stable Transformer on achallenging DMLab30 environment. The code for all our experiments and models isavailable at https://github.com/jerrodparker20/adaptive-transformers-in-rl.

Adaptive Transformers in RL