A5001980621- Reinforcement Learning in Robotics
- Artificial Intelligence in Games
- Computability, Logic, AI Algorithms
- Neural dynamics and brain function
- Topic Modeling
- AI-based Problem Solving and Planning
- Intelligent Tutoring Systems and Adaptive Learning
- Teaching and Learning Programming
- Neural and Behavioral Psychology Studies
- Educational Games and Gamification
- Evolutionary Algorithms and Applications
- Child and Animal Learning Development
- Explainable Artificial Intelligence (XAI)
- Functional Brain Connectivity Studies
- Sports Analytics and Performance
- Neural Networks and Applications
Harvard University Press
2024
Harvard University
2017-2023
Center for Pain and the Brain
2021-2022
Reinforcement learning (RL) studies how an agent comes to achieve reward in environment through interactions over time. Recent advances machine RL have surpassed human expertise at the world's oldest board games and many classic video games, but they require vast quantities of experience learn successfully -- none today's algorithms account for ability so different tasks, quickly. Here we propose a new approach this challenge based on particularly strong form model-based which call...
Abstract Flexibility is one of the hallmarks human problem‐solving. In everyday life, people adapt to changes in common tasks with little no additional training. Much existing work on flexibility problem‐solving has focused how new domains by drawing solutions from previously learned domains. real‐world tasks, however, humans must generalize across a wide range within‐domain variation. this we argue that representational abstraction plays an important role such generalization. We then...
Abstract Humans learn internal models of the environment that support efficient planning and flexible generalization in complex, real-world domains. Yet it remains unclear how such are represented learned brain. We approach this question within framework theory-based reinforcement learning, a strong form model-based learning which model is an intuitive theory – rich, abstract, causal built on natural ontology physical objects, intentional agents, relations, goals. used to analyze brain data...