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Unveiling the Role of Expert Guidance: A Comparative Analysis of User-centered Imitation Learning and Traditional Reinforcement Learning

Amr Gomaa, Bilal Mahdy

TL;DR

The insights gained from this study contribute to the advancement of human-centered artificial intelligence by highlighting the benefits and challenges associated with the incorporation of human feedback into the learning process.

Abstract

Integration of human feedback plays a key role in improving the learning capabilities of intelligent systems. This comparative study delves into the performance, robustness, and limitations of imitation learning compared to traditional reinforcement learning methods within these systems. Recognizing the value of human-in-the-loop feedback, we investigate the influence of expert guidance and suboptimal demonstrations on the learning process. Through extensive experimentation and evaluations conducted in a pre-existing simulation environment using the Unity platform, we meticulously analyze the effectiveness and limitations of these learning approaches. The insights gained from this study contribute to the advancement of human-centered artificial intelligence by highlighting the benefits and challenges associated with the incorporation of human feedback into the learning process. Ultimately, this research promotes the development of models that can effectively address complex real-world problems.

Unveiling the Role of Expert Guidance: A Comparative Analysis of User-centered Imitation Learning and Traditional Reinforcement Learning

TL;DR

The insights gained from this study contribute to the advancement of human-centered artificial intelligence by highlighting the benefits and challenges associated with the incorporation of human feedback into the learning process.

Abstract

Integration of human feedback plays a key role in improving the learning capabilities of intelligent systems. This comparative study delves into the performance, robustness, and limitations of imitation learning compared to traditional reinforcement learning methods within these systems. Recognizing the value of human-in-the-loop feedback, we investigate the influence of expert guidance and suboptimal demonstrations on the learning process. Through extensive experimentation and evaluations conducted in a pre-existing simulation environment using the Unity platform, we meticulously analyze the effectiveness and limitations of these learning approaches. The insights gained from this study contribute to the advancement of human-centered artificial intelligence by highlighting the benefits and challenges associated with the incorporation of human feedback into the learning process. Ultimately, this research promotes the development of models that can effectively address complex real-world problems.

Paper Structure

This paper contains 9 sections, 3 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction and Related Work
  2. Methodology
  3. Base Environment (Low-complexity Environment).
  4. Colored Environment (Medium-complexity Environment).
  5. Limited Ammunition with Multiple Bird Environment (High Complexity Environment).
  6. Discussion and Results
  7. Low and Medium Complexity Setting
  8. High Complexity Setting
  9. Conclusion and Future Work

Figures (5)

  • Figure 1: Screenshots (not to scale) of the Bird Hunter game showing the base environment (left), the grayscale backdrop camera view (middle), and the high-complexity environment (right).
  • Figure 2: Comparing base environment with the RGB environment for different RL algorithms.
  • Figure 3: Comparing different IL algorithms in the RGB environment.
  • Figure 4: Comparing imitation learning (BC + GAIL) for a good demonstration (i.e., expert user) and suboptimal demonstration (i.e., novice user).
  • Figure 5: Comparison of average cumulative reward, episode length and model entropy between RL, BC and GAIL in the Limited Ammo environment