A Systematic Approach to Design Real-World Human-in-the-Loop Deep Reinforcement Learning: Salient Features, Challenges and Trade-offs (2504.17006v1)

Abstract: With the growing popularity of deep reinforcement learning (DRL), human-in-the-loop (HITL) approach has the potential to revolutionize the way we approach decision-making problems and create new opportunities for human-AI collaboration. In this article, we introduce a novel multi-layered hierarchical HITL DRL algorithm that comprises three types of learning: self learning, imitation learning and transfer learning. In addition, we consider three forms of human inputs: reward, action and demonstration. Furthermore, we discuss main challenges, trade-offs and advantages of HITL in solving complex problems and how human information can be integrated in the AI solution systematically. To verify our technical results, we present a real-world unmanned aerial vehicles (UAV) problem wherein a number of enemy drones attack a restricted area. The objective is to design a scalable HITL DRL algorithm for ally drones to neutralize the enemy drones before they reach the area. To this end, we first implement our solution using an award-winning open-source HITL software called Cogment. We then demonstrate several interesting results such as (a) HITL leads to faster training and higher performance, (b) advice acts as a guiding direction for gradient methods and lowers variance, and (c) the amount of advice should neither be too large nor too small to avoid over-training and under-training. Finally, we illustrate the role of human-AI cooperation in solving two real-world complex scenarios, i.e., overloaded and decoy attacks.

Overview of Human-in-the-Loop Framework for Real-World Deep Reinforcement Learning

The paper presented proposes a human-in-the-loop (HITL) framework for the enhancement of deep reinforcement learning (DRL) algorithms applied to real-world decision-making tasks. This framework integrates human input into the learning process, aiming to harness human intelligence alongside machine learning capabilities to facilitate the development of DRL agents. Given the complexities inherent in HITL systems, particularly regarding the interplay between human information and AI, the authors introduce a hierarchical learning algorithm that addresses these challenges by combining elements of self-learning, imitation learning, and transfer learning.

Key Components and Methodology

To structure this approach, the paper details a three-tiered HITL learning algorithm which is employed to manage a multi-agent environment involving unmanned aerial vehicles (UAVs) tasked with neutralizing enemy drones. This hierarchical setup capitalizes on different learning paradigms and human interactions, delineated as follows:

1. Self-learning: Facilitating autonomous learning in scenarios deprived of human inputs.
2. Imitation learning: Modeling strategies based on human actions where suitable data is accessible.
3. Transfer learning: Transmitting acquired insights across diverse applications within the real-world context.

The operational framework for these layers utilizes three types of human inputs—rewards, actions, and demonstrations—to improve learning efficacy and agent performance. The practical implications of HITL are examined through the UAV task, which presents various issues regarding human interaction integration in AI processes.

Practical Insights and Numerical Results

The experimental component verifies the framework with simulations regarding UAV control, revealing significant findings:

• Improved Training Efficiency: Utilization of human inputs led to expedited training processes, achieving higher overall performance compared to traditional AI-only systems.
• Guidance Influence: Human advice acted as a directional guide for adaptability and minimization of variance in gradient methods. The findings illustrate optimal balance in the amount of human advice necessary to avoid over-training or under-training scenarios.
• Enhanced Scalability: Algorithm effectiveness was sustained across increasing numbers of both ally drones and adversarial threats (i.e., overloaded attacks), emphasizing modular scalability.
• Complex Attack Handling: HITL demonstrated robustness in complex scenarios such as decoy attacks, wherein conventional DRL approaches failed. This underscores the importance of human-like contextual awareness for nuanced real-world challenges.

Theoretical and Future Implications

The proposed HITL framework underscores a strategic advancement in DRL methodologies, integrating human expertise within AI learning paradigms to effectively tackle intricate decision-making tasks. This hybrid approach projects substantial theoretical implications for the AI domain, suggesting avenues for fortifying AI systems against uncertain environments through human collaboration.

Furthermore, future research directions may explore optimizing human and AI learning reciprocity, where AI might also assist human operators, enriching expertise through collaborative experiences. Exploration into finer-grained human guidance models and more sophisticated cooperative models will potentially expand the efficacy and reliability of HITL systems in broader applications.