  At first sight it may seem straightforward to use recurrent layers in Deep
Reinforcement Learning algorithms to enable agents to make use of memory in the
setting of partially observable environments. Starting from widely used
Proximal Policy Optimization (PPO), we highlight vital details that one must
get right when adding recurrence to achieve a correct and efficient
implementation, namely: properly shaping the neural net's forward pass,
arranging the training data, correspondingly selecting hidden states for
sequence beginnings and masking paddings for loss computation. We further
explore the limitations of recurrent PPO by benchmarking the contributed novel
environments Mortar Mayhem and Searing Spotlights that challenge the agent's
memory beyond solely capacity and distraction tasks. Remarkably, we can
demonstrate a transition to strong generalization in Mortar Mayhem when scaling
the number of training seeds, while the agent does not succeed on Searing
Spotlights, which seems to be a tough challenge for memory-based agents.
