Abstraction for Deep Reinforcement Learning

Understand

We characterise the problem of abstraction in the context of deep reinforcement learning.

• Various well established approaches to analogical reasoning and associative memory might be brought to bear on this issue, but they present difficulties because of the need for end-to-end differentiability.
• We review developments in AI and machine learning that could facilitate their adoption.