  Understanding how knowledge about the world is represented within model-free
deep reinforcement learning methods is a major challenge given the black box
nature of its learning process within high-dimensional observation and action
spaces. AlphaStar and OpenAI Five have shown that agents can be trained without
any explicit hierarchical macro-actions to reach superhuman skill in games that
require taking thousands of actions before reaching the final goal. Assessing
the agent's plans and game understanding becomes challenging given the lack of
hierarchy or explicit representations of macro-actions in these models, coupled
with the incomprehensible nature of the internal representations.
  In this paper, we study the distributed representations learned by OpenAI
Five to investigate how game knowledge is gradually obtained over the course of
training. We also introduce a general technique for learning a model from the
agent's hidden states to identify the formation of plans and subgoals. We show
that the agent can learn situational similarity across actions, and find
evidence of planning towards accomplishing subgoals minutes before they are
executed. We perform a qualitative analysis of these predictions during the
games against the DotA 2 world champions OG in April 2019.
