Policy-Aware Model Learning for Policy Gradient Methods
  This paper considers the problem of learning a model in model-based
reinforcement learning (MBRL). We examine how the planning module of an MBRL
algorithm uses the model, and propose that the model learning module should
incorporate the way the planner is going to use the model. This is in contrast
to conventional model learning approaches, such as those based on maximum
likelihood estimate, that learn a predictive model of the environment without
explicitly considering the interaction of the model and the planner. We focus
on policy gradient type of planning algorithms and derive new loss functions
for model learning that incorporate how the planner uses the model. We call
this approach Policy-Aware Model Learning (PAML). We theoretically analyze a
generic model-based policy gradient algorithm and provide a convergence
guarantee for the optimized policy. We also empirically evaluate PAML on some
benchmark problems, showing promising results.
