  In a partially observable Markov decision process (POMDP), an agent typically
uses a representation of the past to approximate the underlying MDP. We propose
to utilize a frozen Pretrained Language Transformer (PLT) for history
representation and compression to improve sample efficiency. To avoid training
of the Transformer, we introduce FrozenHopfield, which automatically associates
observations with pretrained token embeddings. To form these associations, a
modern Hopfield network stores these token embeddings, which are retrieved by
queries that are obtained by a random but fixed projection of observations. Our
new method, HELM, enables actor-critic network architectures that contain a
pretrained language Transformer for history representation as a memory module.
Since a representation of the past need not be learned, HELM is much more
sample efficient than competitors. On Minigrid and Procgen environments HELM
achieves new state-of-the-art results. Our code is available at
https://github.com/ml-jku/helm.
