We study the Stochastic Shortest Path (SSP) problem in which an agent has to reach a goal state in minimum total expected cost. In the learning formulation of the problem, the agent has no prior knowledge about the costs and dynamics of the model. She repeatedly interacts with the model for K episodes, and has to minimize her regret.

The non-stationarity nature and the lack of supervision make the problem challenging to be tackled.

Optimizing Data Collection for Machine Learning

Optimizing Data Collection for Machine Learning