Why policy optimization methods?
In this section, we will cover the pros and cons of policy optimization methods over value-based methods. The advantages are as follows:
- They provides better convergence.
- They are highly effective in case of high-dimensional/continuous state-action spaces. If action spaces are very big then a max function in a value-based method will be computationally expensive. So, the policy-based method directly changes the policy by changing the parameters instead of solving the max function at each step.
- Ability to learn stochastic policies.
The disadvantages associated with policy-based methods are as follows:
- Converges to local instead of global optimum
- Policy evaluation is inefficient and has high variance
We will discuss the approaches to tackle these disadvantages later in this chapter. For now, let's focus on the need for stochastic policies.
Why stochastic policy?
Let's go through two examples that will explain the importance of incorporating a stochastic policy compared...
