Before revision:  We prove that an optimal policy can be learned over this model-invariance state abstraction  . Next, we propose a practical method to approximately learn a model-invariant representation for complex domains . We validate our approach by showing improved modeling performance over standard maximum likelihood approaches on challenging tasks, such as the MuJoCo-based Humanoid.
After revision:  We prove that an optimal policy can be learned over this model-invariance state abstraction . Next, we propose a practical method to approximately learn a model-invariant representation for complex domains and validate our approach by showing improved modeling performance over standard maximum likelihood approaches on challenging tasks, such as the MuJoCo-based Humanoid.