April 2019
Intermediate to advanced
212 pages
5h 34m
English
Content preview from Hands-On Q-Learning with Python
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Optimal versus safe paths – revisiting SARSA
We discussed optimal goal-seeking strategies in the context of the MABP, but let's now discuss them more generally:
As we briefly discussed in Chapter 1, Brushing Up on Reinforcement Learning Concepts, regarding the differences between Q-learning and State-Action-Reward-State-Action (SARSA), we can sum those differences up as follows: Q-learning takes the optimal path to the goal, while SARSA takes a suboptimal but safer path, with less risk of taking highly suboptimal actions.
In the well-known cliff-walking problem, the goal is to start at the bottom left square in the preceding diagram and ...
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