7 Achieving goals more effectively and efficiently
In this chapter
- You learn about making reinforcement learning agents more effective at reaching optimal performance when interacting with challenging environments.
- You learn about making reinforcement learning agents more efficient at achieving goals by making the most from the experiences.
- You improve on the agents presented in the previous chapters to have them make the most out of the data they collect and therefore optimize their performance more quickly.
Efficiency is doing things right; effectiveness is doing the right things.
— Peter Drucker, Founder of modern Management and, Presidential Medal of Freedom recipient
In this chapter, we improve on the agents you learned about in the previous chapter. More specifically, we take on two separate lines of improvement. First, we use the λ return that you learned about in chapter 5 for the policy evaluation requirements of the generalized policy iteration pattern. We explore using the λ return for both on-policy and off-policy methods. Using the λ return with eligibility traces propagates credit to the right state-action pairs more quickly than standard methods, making the value-function estimates get near the actual values faster.