Online Learning and Planning in Partially Observable Domains without
  Prior Knowledge
  How an agent can act optimally in stochastic, partially observable domains is
a challenge problem, the standard approach to address this issue is to learn
the domain model firstly and then based on the learned model to find the (near)
optimal policy. However, offline learning the model often needs to store the
entire training data and cannot utilize the data generated in the planning
phase. Furthermore, current research usually assumes the learned model is
accurate or presupposes knowledge of the nature of the unobservable part of the
world. In this paper, for systems with discrete settings, with the benefits of
Predictive State Representations~(PSRs), a model-based planning approach is
proposed where the learning and planning phases can both be executed online and
no prior knowledge of the underlying system is required. Experimental results
show compared to the state-of-the-art approaches, our algorithm achieved a high
level of performance with no prior knowledge provided, along with theoretical
advantages of PSRs. Source code is available at
https://github.com/DMU-XMU/PSR-MCTS-Online.
