Deep Q-learning from Demonstrations (1704.03732v4)

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.

Deep Q-Learning from Demonstrations: Insights and Implications

The paper "Deep Q-Learning from Demonstrations" presents an innovative reinforcement learning (RL) approach that leverages human demonstration data to expedite and enhance the learning process of deep Q-learning algorithms. Traditional deep reinforcement learning models, despite their successes, often demand extensive data and endure suboptimal performance during initial training phases. This inherent inefficiency presents significant challenges for real-world applications where learning must occur directly in the operating environment, such as in autonomous vehicles or real-time control systems.

Algorithmic Innovation and Performance

The core contribution is the introduction of the Deep Q-learning from Demonstrations (DQfD) algorithm. DQfD utilizes small amounts of demonstration data to significantly accelerate learning, even with limited demonstration data volume, by integrating a prioritized replay mechanism with temporal difference updates and supervised classification of actions. This paper highlights that DQfD showcases superior initial performance in 41 out of 42 tested games compared to Prioritized Dueling Double Deep Q-Networks (PDD DQN).

A notable point is DQfD's efficiency in overtaking the best demonstration performance in 14 out of 42 games, indicating not just faster learning but also a potential to exceed the reference provided by demonstrations. Additionally, in 11 games, DQfD leveraged human demonstrations to achieve state-of-the-art results, underscoring the practical applications where demonstration data can compensate for exploratory inadequacies.

Technical Merits

DQfD's architecture is rooted in various key modifications:

• Pre-training Phase: Initiates learning exclusively from demonstration data, effectively setting a strong performance baseline through a combined loss framework involving temporal difference, supervised classification, n-step returns, and L2 regularization.
• Prioritized Replay Mechanism: Adjusts the sampling of demonstration versus self-generated data dynamically, rooted in transition priorities, a critical aspect that ensures learning efficiency.
• Robustness in Evaluation: The paper presents comprehensive evaluations against existing and related methods, demonstrating that DQfD not only matches but often surpasses the performance of models relying solely on RL or imitation.

Implications for Future Research

The implications of this work extend beyond technical improvements in learning efficiency. By showing how limited human demonstration data can optimize performance in environments with sparse rewards or complex dynamics, DQfD paves the way towards practical RL applications where simulation-based exploration is unfeasible or inaccurate.

Further exploration could involve mapping these insights to continuous action spaces, increasing the breadth of tasks applicable for DQfD. Another line of research could focus on quantifying and optimizing the demonstration data's volume and quality to further enhance learning performance and resilience.

Closing Thoughts

While DQfD represents a significant step towards more efficient and practically applicable RL systems, it highlights the importance of demonstration data as a powerful tool in RL. The model's success suggests a broader exploration of hybrid models that combine demonstration-driven and reward-driven approaches, potentially leading to more adaptable and intelligent systems capable of operating in diverse real-world contexts.

The research community can draw numerous insights from this work, particularly in designing RL systems that require faster convergence and robustness in dynamic environments. As algorithmic developments continue, the holistic integration of varying data types into RL frameworks remains a promising avenue for future breakthroughs.