Observational Learning: Imitation Through an Adaptive Probabilistic Approach

This paper proposes an adaptive method to enable imitation learning from expert demonstrations in a multi-agent context. Our work employs the inverse reinforcement learning method to a coupled Dynamic Bayesian Network to facilitate dynamic learning in an interactive system. This method studies the interaction at both discrete and continuous levels by identifying inter-relationships between the objects to facilitates the prediction of an expert agent’s demonstrations. We evaluate the learning procedure in the scene of learner agent based on probabilistic reward function. Our goal is to estimate policies that predicted trajectories match the observed one by minimizing the Kullback- Leiber divergence. The reward policies provide a probabilistic dynamic structure to minimize the abnormalities.