Imitation learning: A survey of learning methods
Imitation learning techniques aim to mimic human behavior in a given task. An agent (a
learning machine) is trained to perform a task from demonstrations by learning a mapping …
learning machine) is trained to perform a task from demonstrations by learning a mapping …
[HTML][HTML] A survey of demonstration learning
A Correia, LA Alexandre - Robotics and Autonomous Systems, 2024 - Elsevier
With the fast improvement of machine learning, reinforcement learning (RL) has been used
to automate human tasks in different areas. However, training such agents is difficult and …
to automate human tasks in different areas. However, training such agents is difficult and …
A generalized apprenticeship learning framework for modeling heterogeneous student pedagogical strategies
A key challenge in e-learning environments like Intelligent Tutoring Systems (ITSs) is to
induce effective pedagogical policies efficiently. While Deep Reinforcement Learning (DRL) …
induce effective pedagogical policies efficiently. While Deep Reinforcement Learning (DRL) …
[PDF][PDF] Hierarchical Apprenticeship Learning for Disease Progression Modeling.
Disease progression modeling (DPM) plays an essential role in characterizing patients'
historical pathways and predicting their future risks. Apprenticeship learning (AL) aims to …
historical pathways and predicting their future risks. Apprenticeship learning (AL) aims to …
An offline time-aware apprenticeship learning framework for evolving reward functions
Apprenticeship learning (AL) is a process of inducing effective decision-making policies via
observing and imitating experts' demonstrations. Most existing AL approaches, however, are …
observing and imitating experts' demonstrations. Most existing AL approaches, however, are …
Deep reward shaping from demonstrations
Deep reinforcement learning is rapidly gaining attention due to recent successes in a variety
of problems. The combination of deep learning and reinforcement learning allows for a …
of problems. The combination of deep learning and reinforcement learning allows for a …
Solving the shepherding problem: Imitation learning can acquire the switching algorithm
A single shepherd dog can herd a flock of sheep to a gate. Despite a heuristic algorithm of a
dog based on adaptive switching between collecting the sheep when they are too dispersed …
dog based on adaptive switching between collecting the sheep when they are too dispersed …
Augmented behavioral cloning from observation
Imitation from observation is a computational technique that teaches an agent on how to
mimic the behavior of an expert by observing only the sequence of states from the expert …
mimic the behavior of an expert by observing only the sequence of states from the expert …
Imitating unknown policies via exploration
Behavioral cloning is an imitation learning technique that teaches an agent how to behave
through expert demonstrations. Recent approaches use self-supervision of fully-observable …
through expert demonstrations. Recent approaches use self-supervision of fully-observable …
Modeling autonomous vehicle responses to novel observations using hierarchical cognitive representations inspired active inference
Equipping autonomous agents for dynamic interaction and navigation is a significant
challenge in intelligent transportation systems. This study aims to address this by …
challenge in intelligent transportation systems. This study aims to address this by …