Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Imitation in animals and artifacts
Imitation in animals and artifacts
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Accelerating reinforcement learning through implicit imitation
Journal of Artificial Intelligence Research
A Bayesian approach to imitation in reinforcement learning
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Model based Bayesian exploration
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Relational Learning by Imitation
KES-AMSTA '09 Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
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Imitation-based learning is a general mechanism for rapid acquisition of new behaviors in autonomous agents and robots. In this paper, we propose a new approach to learning by imitation based on parameter learning in probabilistic graphical models. Graphical models are used not only to model an agent's own dynamics but also the dynamics of an observed teacher. Parameter tying between the agent-teacher models ensures consistency and facilitates learning. Given only observations of the teacher's states, we use the expectation-maximization (EM) algorithm to learn both dynamics and policies within graphical models. We present results demonstrating that EM-based imitation learning outperforms pure exploration-based learning on a benchmark problem (the FlagWorld domain). We additionally show that the graphical model representation can be leveraged to incorporate domain knowledge (e.g., state space factoring) to achieve significant speed-up in learning.