  Typical reinforcement learning (RL) agents learn to complete tasks specified
by reward functions tailored to their domain. As such, the policies they learn
do not generalize even to similar domains. To address this issue, we develop a
framework through which a deep RL agent learns to generalize policies from
smaller, simpler domains to more complex ones using a recurrent attention
mechanism. The task is presented to the agent as an image and an instruction
specifying the goal. This meta-controller guides the agent towards its goal by
designing a sequence of smaller subtasks on the part of the state space within
the attention, effectively decomposing it. As a baseline, we consider a setup
without attention as well. Our experiments show that the meta-controller learns
to create subgoals within the attention.
