Blending Autonomous Exploration and Apprenticeship Learning

2011-12-01 · NeurIPS 2011 12 · Thomas J. Walsh, Daniel K. Hewlett, Clayton T. Morrison

We present theoretical and empirical results for a framework that combines the benefits of apprenticeship and autonomous reinforcement learning. Our approach modifies an existing apprenticeship learning framework that relies on teacher demonstrations and does not necessarily explore the environment. The first change is replacing previously used Mistake Bound model learners with a recently proposed framework that melds the KWIK and Mistake Bound supervised learning protocols. The second change is introducing a communication of expected utility from the student to the teacher. The resulting system only uses teacher traces when the agent needs to learn concepts it cannot efficiently learn on its own.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

From Answer Givers to Design Mentors: Guiding LLMs with the Cognitive Apprenticeship Model

2026-01-27 · Yongsu Ahn, Lejun R Liao, Benjamin Bach, Nam Wook Kim arxiv

Design feedback helps practitioners improve their artifacts while also fostering reflection and design reasoning. Large Language Models (LLMs) such as ChatGPT can support design work, but often provide generic, one-off s…

Online Apprenticeship Learning

2021-02-13 · Lior Shani, Tom Zahavy, Shie Mannor

In Apprenticeship Learning (AL), we are given a Markov Decision Process (MDP) without access to the cost function. Instead, we observe trajectories sampled by an expert that acts according to some policy. The goal is to …

Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations

2019-06-14 · NeurIPS 2020 12 · Rohan Paleja, Andrew Silva, Letian Chen, Matthew Gombolay

Resource scheduling and coordination is an NP-hard optimization requiring an efficient allocation of agents to a set of tasks with upper- and lower bound temporal and resource constraints. Due to the large-scale and dyna…

Decision Making Scheduling

Unknown mixing times in apprenticeship and reinforcement learning

2019-05-23 · Tom Zahavy, Alon Cohen, Haim Kaplan, Yishay Mansour

We derive and analyze learning algorithms for apprenticeship learning, policy evaluation, and policy gradient for average reward criteria. Existing algorithms explicitly require an upper bound on the mixing time. In cont…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Enabling Autonomous Navigation in a Snake Robot through Visual-Inertial Odometry and Closed-Loop Trajectory Tracking Control

2025-12-09 · Mohammed Irfan Ali arxiv

Snake robots offer exceptional mobility across extreme terrain inaccessible to conventional rovers, yet their highly articulated bodies present fundamental challenges for autonomous navigation in environments lacking ext…

Robot Navigation