  Humans and animals solve a difficult problem much more easily when they are
presented with a sequence of problems that starts simple and slowly increases
in difficulty. We explore this idea in the context of reinforcement learning.
Rather than providing the agent with an externally provided curriculum of
progressively more difficult tasks, the agent solves a single task utilizing a
decreasingly constrained policy space. The algorithm we propose first learns to
categorize features into positive and negative before gradually learning a more
refined policy. Experimental results in Tetris demonstrate superior learning
rate of our approach when compared to existing algorithms.
