Deep Q-learning from Demonstrations

TL;DR

This paper introduces Deep Q-learning from Demonstrations (DQfD), an algorithm that significantly accelerates deep reinforcement learning by leveraging demonstration data, outperforming prior methods and achieving state-of-the-art results in many games.

Contribution

The paper proposes DQfD, a novel algorithm combining demonstration data with deep Q-learning, improving learning speed and performance in complex environments.

Findings

DQfD outperforms PDD DQN in initial performance on 41 of 42 games.

DQfD learns to outperform the best demonstration in 14 games.

DQfD achieves state-of-the-art results in 11 games.

Abstract

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.