Muhan Hou

Bernhard Hilpert, Muhan Hou, Kim Baraka et al.

Reinforcement Learning (RL) agents often exhibit learning behaviors that are not intuitively interpretable by human observers, which can result in suboptimal feedback in collaborative teaching settings. Yet, how humans perceive and interpret RL agent's learning behavior is largely unknown. In a bottom-up approach with two experiments, this work provides a data-driven understanding of the factors of human observers' understanding of the agent's learning process. A novel, observation-based paradigm to directly assess human inferences about agent learning was developed. In an exploratory interview study (\textit{N}=9), we identify four core themes in human interpretations: Agent Goals, Knowledge, Decision Making, and Learning Mechanisms. A second confirmatory study (\textit{N}=34) applied an expanded version of the paradigm across two tasks (navigation/manipulation) and two RL algorithms (tabular/function approximation). Analyses of 816 responses confirmed the reliability of the paradigm and refined the thematic framework, revealing how these themes evolve over time and interrelate. Our findings provide a human-centered understanding of how people make sense of agent learning, offering actionable insights for designing interpretable RL systems and improving transparency in Human-Robot Interaction.

Muhan Hou, Koen Hindriks, A. E. Eiben et al.

Transfer Learning (TL) is a powerful tool that enables robots to transfer learned policies across different environments, tasks, or embodiments. To further facilitate this process, efforts have been made to combine it with Learning from Demonstrations (LfD) for more flexible and efficient policy transfer. However, these approaches are almost exclusively limited to offline demonstrations collected before policy transfer starts, which may suffer from the intrinsic issue of covariance shift brought by LfD and harm the performance of policy transfer. Meanwhile, extensive work in the learning-from-scratch setting has shown that online demonstrations can effectively alleviate covariance shift and lead to better policy performance with improved sample efficiency. This work combines these insights to introduce online demonstrations into a policy transfer setting. We present Policy Transfer with Online Demonstrations, an active LfD algorithm for policy transfer that can optimize the timing and content of queries for online episodic expert demonstrations under a limited demonstration budget. We evaluate our method in eight robotic scenarios, involving policy transfer across diverse environment characteristics, task objectives, and robotic embodiments, with the aim to transfer a trained policy from a source task to a related but different target task. The results show that our method significantly outperforms all baselines in terms of average success rate and sample efficiency, compared to two canonical LfD methods with offline demonstrations and one active LfD method with online demonstrations. Additionally, we conduct preliminary sim-to-real tests of the transferred policy on three transfer scenarios in the real-world environment, demonstrating the policy effectiveness on a real robot manipulator.

Muhan Hou, Koen Hindriks, A. E. Eiben et al.

Reinforcement Learning (RL) has achieved great success in sequential decision-making problems, but often at the cost of a large number of agent-environment interactions. To improve sample efficiency, methods like Reinforcement Learning from Expert Demonstrations (RLED) introduce external expert demonstrations to facilitate agent exploration during the learning process. In practice, these demonstrations, which are often collected from human users, are costly and hence often constrained to a limited amount. How to select the best set of human demonstrations that is most beneficial for learning therefore becomes a major concern. This paper presents EARLY (Episodic Active Learning from demonstration querY), an algorithm that enables a learning agent to generate optimized queries of expert demonstrations in a trajectory-based feature space. Based on a trajectory-level estimate of uncertainty in the agent's current policy, EARLY determines the optimized timing and content for feature-based queries. By querying episodic demonstrations as opposed to isolated state-action pairs, EARLY improves the human teaching experience and achieves better learning performance. We validate the effectiveness of our method in three simulated navigation tasks of increasing difficulty. The results show that our method is able to achieve expert-level performance for all three tasks with convergence over 30\% faster than other baseline methods when demonstrations are generated by simulated oracle policies. The results of a follow-up pilot user study (N=18) further validate that our method can still maintain a significantly better convergence in the case of human expert demonstrators while achieving a better user experience in perceived task load and consuming significantly less human time.

Muhan Hou, Zonghao Mu, Jing Li et al.

The guiding task of a mobile robot requires not only human-aware navigation, but also appropriate yet timely interaction for active instruction. State-of-the-art tour-guide models limit their socially-aware consideration to adapting to users' motion, ignoring the interactive behavior planning to fulfill the communicative demands. We propose a multi-behavior planning framework based on Monte Carlo Tree Search to better assist users to understand confusing scene contexts, select proper paths and timely arrive at the destination. To provide proactive guidance, we construct a sampling-based probability model of human motion to consider the interrelated effects between robots and humans. We validate our method both in simulation and real-world experiments along with performance comparison with state-of-the-art models.