Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning
TrailBlazer is a new algorithm for sample-efficient Monte-Carlo planning in Markov decision processes (MDPs) that use generative models. It finds near-optimal value function approximations with polynomial sample complexity bounds by exploiting the structure of near-optimal states. This allows for computationally efficient planning when using generative models.