Bootstrapping Skills

Understand

The monolithic approach to policy representation in Markov Decision Processes (MDPs) looks for a single policy that can be represented as a function from states to actions.

• For the monolithic approach to succeed (and this is not always possible), a complex feature representation is often necessary since the policy is a complex object that has to prescribe what actions to take all over the state space.
• This is especially true in large domains with complicated dynamics.
• It is also computationally inefficient to both learn and plan in MDPs using a complex monolithic approach.