  Deep reinforcement learning (RL) has achieved several high profile successes
in difficult decision-making problems. However, these algorithms typically
require a huge amount of data before they reach reasonable performance. In
fact, their performance during learning can be extremely poor. This may be
acceptable for a simulator, but it severely limits the applicability of deep RL
to many real-world tasks, where the agent must learn in the real environment.
In this paper we study a setting where the agent may access data from previous
control of the system. We present an algorithm, Deep Q-learning from
Demonstrations (DQfD), that leverages small sets of demonstration data to
massively accelerate the learning process even from relatively small amounts of
demonstration data and is able to automatically assess the necessary ratio of
demonstration data while learning thanks to a prioritized replay mechanism.
DQfD works by combining temporal difference updates with supervised
classification of the demonstrator's actions. We show that DQfD has better
initial performance than Prioritized Dueling Double Deep Q-Networks (PDD DQN)
as it starts with better scores on the first million steps on 41 of 42 games
and on average it takes PDD DQN 83 million steps to catch up to DQfD's
performance. DQfD learns to out-perform the best demonstration given in 14 of
42 games. In addition, DQfD leverages human demonstrations to achieve
state-of-the-art results for 11 games. Finally, we show that DQfD performs
better than three related algorithms for incorporating demonstration data into
DQN.
