April 2019
Intermediate to advanced
212 pages
5h 34m
English
Every time your agent steps through the environment, it will update the Q-table with the rewards it has received. Once your Q-table stops updating and reaches its final state, we will know that your agent has found the optimal path to its destination. It will have solved the MDP represented by its environment.
What this means in practice is that your agent will have found the best actions to take from each state that it has encountered through its exploration of its environment. It will have learned enough about the environment to have found an optimal strategy for navigating a path to the goal. When your Q-table stops updating, we say that it has converged to its final state.
We can be sure that when the ...
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