Policy-shaped prediction: avoiding distractions in model-based
reinforcement learning
Policy-shaped prediction: avoiding distractions in model-based
reinforcement learning
Model-based reinforcement learning (MBRL) is a promising route to sample-efficient policy optimization. However, a known vulnerability of reconstruction-based MBRL consists of scenarios in which detailed aspects of the world are highly predictable, but irrelevant to learning a good policy. Such scenarios can lead the model to exhaust its capacity on …