Title: Memory as State Abstraction Over History
Primary area: reinforcement learning
Keywords: reinforcement learning, memory, rl, sequential decision making, state abstraction, abstraction, hierarchy, pomdp

Abstract: Reinforcement learning is provably difficult in non-Markovian environments, which motivates identifying useful environment classes. Previous work has identified classes such as regular decision processes and approximate information states. While these works address essential properties such as tractability, they do not answer how the classes relate, or when users should prefer one class over another. We resolve this by defining finer POMDP classes in terms of memory and state abstractions. Considering agent memory as a temporally-extended abstraction over the agent's observation-action history, we prove that POMDP classes can be defined using traditional state abstractions, such as model-preservation, optimal value $Q^{\*}$ preservation, and optimal policy $\pi^{\*}$ preservation. In the process, we extend state abstraction to "soft" (stochastic) abstractions and show how this kind of abstraction relates to stochastic memory. Reinterpreting existing POMDP classes using our unified framework enables us to prove new relationships between existing classes and generalize these classes to approximate variants.

TL;DR: We view memory as temporally-extended state abstraction to organize and extend POMDP classes in the literature.