Reinforcement learning agents deployed in the real world often have to cope with partially
observable environments. Therefore, most agents employ memory mechanisms to …

Partially observable environments in reinforcement learning are particularly challenging to
solve because they require the agent to estimate the true state of the environment by …

Abstract Memory capabilities in Deep Reinforcement Learning (DRL) agents have become
increasingly crucial, especially in tasks characterized by partial observability or imperfect …