  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.
