Title: BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning,
Abstract: We introduce a novel method to train agents of reinforcement learning (RL) by
sharing knowledge in a way similar to the concept of using a book. The recorded
information in the form of a book is the main means by which humans learn
knowledge. Nevertheless, the conventional deep RL methods have mainly focused
either on experiential learning where the agent learns through interactions
with the environment from the start or on imitation learning that tries to
mimic the teacher. Contrary to these, our proposed book learning shares key
information among different agents in a book-like manner by delving into the
following two characteristic features: (1) By defining the linguistic function,
input states can be clustered semantically into a relatively small number of
core clusters, which are forwarded to other RL agents in a prescribed manner.
(2) By defining state priorities and the contents for recording, core
experiences can be selected and stored in a small container. We call this
container as `BOOK'. Our method learns hundreds to thousand times faster than
the conventional methods by learning only a handful of core cluster
information, which shows that deep RL agents can effectively learn through the
shared knowledge from other agents.