Interactive Policy Learning through Confidence-Based Autonomy (1401.3439v1)

Abstract: We present Confidence-Based Autonomy (CBA), an interactive algorithm for policy learning from demonstration. The CBA algorithm consists of two components which take advantage of the complimentary abilities of humans and computer agents. The first component, Confident Execution, enables the agent to identify states in which demonstration is required, to request a demonstration from the human teacher and to learn a policy based on the acquired data. The algorithm selects demonstrations based on a measure of action selection confidence, and our results show that using Confident Execution the agent requires fewer demonstrations to learn the policy than when demonstrations are selected by a human teacher. The second algorithmic component, Corrective Demonstration, enables the teacher to correct any mistakes made by the agent through additional demonstrations in order to improve the policy and future task performance. CBA and its individual components are compared and evaluated in a complex simulated driving domain. The complete CBA algorithm results in the best overall learning performance, successfully reproducing the behavior of the teacher while balancing the tradeoff between number of demonstrations and number of incorrect actions during learning.

• The paper introduces Confidence-Based Autonomy (CBA) that minimizes redundant demonstrations by using dynamic confidence thresholds.
• It details Confident Execution and Corrective Demonstration components that effectively balance autonomous decision-making with human expertise.
• Empirical results in a simulated driving task show that CBA can achieve a 0% collision rate, enhancing safety and learning efficiency.

Analysis of Confidence-Based Autonomy for Interactive Policy Learning

The paper presents an innovative approach called Confidence-Based Autonomy (CBA) for interactive policy learning from demonstration. This algorithm is particularly noteworthy in the landscape of machine learning and robotics, where learning from demonstration (LfD) offers a more intuitive alternative to traditional reinforcement learning by directly leveraging the knowledge of human experts. The CBA framework advances the field by optimizing the interaction between human demonstrators and autonomous learning agents, focusing on precise demonstration selection to enhance learning efficiency.

CBA encapsulates two core components: Confident Execution (CE) and Corrective Demonstration (CD). Each is designed to leverage the respective strengths of human teachers and autonomous systems. The Confident Execution component enables an agent to autonomously determine which situations require human intervention. This is achieved by using a dynamic confidence threshold to solicit demonstrations selectively, aiming to minimize the number of demonstrations needed for effective learning. Results in the paper confirm that CBA generally requires fewer demonstrations than when a human teacher decides which states to demonstrate manually.

Corrective Demonstration augments this by allowing human instructors to intervene and correct inaccuracies in the learned policy. This is essential for fine-tuning the agent's performance and rectifying training errors due to data inconsistencies or overgeneralization of the learned policy. Together, these components create a robust framework where the agent can effectively learn complex tasks, such as a simulated driving task, more rapidly and accurately than would be achievable through uniform demonstration or independent exploration alone.

The empirical validation of CBA done in a simulated car driving domain highlights the system's efficacy and efficiency. Specifically, the complete CBA algorithm achieved a 0% collision rate on the driving evaluation, indicating its capacity to learn complex decision-making tasks safely and effectively. Notably, the experiments demonstrated that the use of multiple adjustable confidence thresholds significantly reduces demonstration redundancy, focusing the learning on critical erroneous or uncertain decisions.

Theoretical implications of CBA extend beyond its immediate application. By combining elements of active learning and reinforcement learning with LfD, this approach hints at future systems where autonomous agents might learn efficiently in real-world applications with minimal human oversight. The method's adaptive element, particularly through its dual-threshold mechanism, presents a compelling model for real-time, interactive machine learning scenarios.

Looking forward, the expansive arena of AI invites further exploration into CBA's potential applications and improvements. An intriguing direction includes enhancing the complexity of agent interaction with human teachers, potentially through dialog-based systems for clarification and advice, which may further decrease the need for extensive demonstrations. Additionally, applying this adaptive learning strategy to more diverse robotic tasks, especially those demanding cooperative multi-agent operations, could unlock new capabilities in robotic autonomy.

In conclusion, the CBA framework offers a significant advance in interactive policy learning, thoughtfully combining human intuitional strength with algorithmic rigor to minimize the demands on human effort while maximizing learning outcomes. It sets an important precedent for developing AI systems that are not only environmentally aware and interactive but also efficient learners capable of complex task execution.