  In this paper, we propose to combine imitation and reinforcement learning via
the idea of reward shaping using an oracle. We study the effectiveness of the
near-optimal cost-to-go oracle on the planning horizon and demonstrate that the
cost-to-go oracle shortens the learner's planning horizon as function of its
accuracy: a globally optimal oracle can shorten the planning horizon to one,
leading to a one-step greedy Markov Decision Process which is much easier to
optimize, while an oracle that is far away from the optimality requires
planning over a longer horizon to achieve near-optimal performance. Hence our
new insight bridges the gap and interpolates between imitation learning and
reinforcement learning. Motivated by the above mentioned insights, we propose
Truncated HORizon Policy Search (THOR), a method that focuses on searching for
policies that maximize the total reshaped reward over a finite planning horizon
when the oracle is sub-optimal. We experimentally demonstrate that a
gradient-based implementation of THOR can achieve superior performance compared
to RL baselines and IL baselines even when the oracle is sub-optimal.

Machine Learning;Machine Learning