Unveiling the Role of Expert Guidance: A Comparative Analysis of User-centered Imitation Learning and Traditional Reinforcement Learning
On the map: Offline RL and imitation learning from data → Imitation learning methods
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 6 of its 17 references that are in the map. First posted on arXiv 28 Oct 2024.
Builds on
6 of 17 references.
Closest ancestors · 1
Same direction, newest first
- 2018Behavioral Cloning from Observation4 May
Other references · 5
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
- 2016Generative Adversarial Imitation Learningcited by 1,597
- 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 406
- 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 183
- 2017Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundarycited by 58
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2019Leveraging Human Guidance for Deep Reinforcement Learning Tasks
- 2024GUIDE: Real-Time Human-Shaped Agents
- 2024Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Framework
- 2017Actor-Critic Reinforcement Learning with Simultaneous Human Control and Feedback
- 2022Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback
- 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges
- 2026AI Coaching for Accelerating Human Skill Development with Reinforcement Learning
- 2018Hierarchical Imitation and Reinforcement Learning