  Reinforcement learning agents often forget details of the past, especially
after delays or distractor tasks. Agents with common memory architectures
struggle to recall and integrate across multiple timesteps of a past event, or
even to recall the details of a single timestep that is followed by distractor
tasks. To address these limitations, we propose a Hierarchical Chunk Attention
Memory (HCAM), which helps agents to remember the past in detail. HCAM stores
memories by dividing the past into chunks, and recalls by first performing
high-level attention over coarse summaries of the chunks, and then performing
detailed attention within only the most relevant chunks. An agent with HCAM can
therefore "mentally time-travel" -- remember past events in detail without
attending to all intervening events. We show that agents with HCAM
substantially outperform agents with other memory architectures at tasks
requiring long-term recall, retention, or reasoning over memory. These include
recalling where an object is hidden in a 3D environment, rapidly learning to
navigate efficiently in a new neighborhood, and rapidly learning and retaining
new object names. Agents with HCAM can extrapolate to task sequences much
longer than they were trained on, and can even generalize zero-shot from a
meta-learning setting to maintaining knowledge across episodes. HCAM improves
agent sample efficiency, generalization, and generality (by solving tasks that
previously required specialized architectures). Our work is a step towards
agents that can learn, interact, and adapt in complex and temporally-extended
environments.

Machine Learning;Artificial Intelligence;Neural and Evolutionary Computing