  Episodic memory plays a crucial role in various cognitive processes, such as
the ability to mentally recall past events. While cognitive science emphasizes
the significance of spatial context in the formation and retrieval of episodic
memory, the current primary approach to implementing episodic memory in AI
systems is through transformers that store temporally ordered experiences,
which overlooks the spatial dimension. As a result, it is unclear how the
underlying structure could be extended to incorporate the spatial axis beyond
temporal order alone and thereby what benefits can be obtained. To address
this, this paper explores the use of Spatially-Aware Transformer models that
incorporate spatial information. These models enable the creation of
place-centric episodic memory that considers both temporal and spatial
dimensions. Adopting this approach, we demonstrate that memory utilization
efficiency can be improved, leading to enhanced accuracy in various
place-centric downstream tasks. Additionally, we propose the Adaptive Memory
Allocator, a memory management method based on reinforcement learning that aims
to optimize efficiency of memory utilization. Our experiments demonstrate the
advantages of our proposed model in various environments and across multiple
downstream tasks, including prediction, generation, reasoning, and
reinforcement learning. The source code for our models and experiments will be
available at https://github.com/junmokane/spatially-aware-transformer.
