  Complex environments and tasks pose a difficult problem for holistic
end-to-end learning approaches. Decomposition of an environment into
interacting controllable and non-controllable objects allows supervised
learning for non-controllable objects and universal value function approximator
learning for controllable objects. Such decomposition should lead to a shorter
learning time and better generalisation capability. Here, we consider
arcade-game environments as sets of interacting objects (controllable,
non-controllable) and propose a set of functional modules that are specialized
on mastering different types of interactions in a broad range of environments.
The modules utilize regression, supervised learning, and reinforcement learning
algorithms. Results of this case study in different Atari games suggest that
human-level performance can be achieved by a learning agent within a human
amount of game experience (10-15 minutes game time) when a proper decomposition
of an environment or a task is provided. However, automatization of such
decomposition remains a challenging problem. This case study shows how a model
of a causal structure underlying an environment or a task can benefit learning
time and generalization capability of the agent, and argues in favor of
exploiting modular structure in contrast to using pure end-to-end learning
approaches.
