  Reinforcement learning (rl) is a popular paradigm for sequential decision
making problems. The past decade's advances in rl have led to breakthroughs in
many challenging domains such as video games, board games, robotics, and chip
design. The sample inefficiency of deep reinforcement learning methods is a
significant obstacle when applying rl to real-world problems. Transfer learning
has been applied to reinforcement learning such that the knowledge gained in
one task can be applied when training in a new task. Curriculum learning is
concerned with sequencing tasks or data samples such that knowledge can be
transferred between those tasks to learn a target task that would otherwise be
too difficult to solve. Designing a curriculum that improves sample efficiency
is a complex problem. In this thesis, we propose a teacher-student curriculum
learning setting where we simultaneously train a teacher that selects tasks for
the student while the student learns how to solve the selected task. Our method
is independent of human domain knowledge and manual curriculum design. We
evaluated our methods on two reinforcement learning benchmarks: grid world and
the challenging Google Football environment. With our method, we can improve
the sample efficiency and generality of the student compared to tabula-rasa
reinforcement learning.
