  This work shows that value-aware model learning, known for its numerous
theoretical benefits, is also practically viable for solving challenging
continuous control tasks in prevalent model-based reinforcement learning
algorithms. First, we derive a novel value-aware model learning objective by
bounding the model-advantage i.e. model performance difference, between two
MDPs or models given a fixed policy, achieving superior performance to prior
value-aware objectives in most continuous control environments. Second, we
identify the issue of stale value estimates in naively substituting value-aware
objectives in place of maximum-likelihood in dyna-style model-based RL
algorithms. Our proposed remedy to this issue bridges the long-standing gap in
theory and practice of value-aware model learning by enabling successful
deployment of all value-aware objectives in solving several continuous control
robotic manipulation and locomotion tasks. Our results are obtained with
minimal modifications to two popular and open-source model-based RL algorithms
-- SLBO and MBPO, without tuning any existing hyper-parameters, while also
demonstrating better performance of value-aware objectives than these baseline
in some environments.
