April 2018
Intermediate to advanced
334 pages
10h 18m
English
With the increase in the number of dimensions, data increases. As a result, there is more computation covering the complete state-action spaces.
Let's take an example:
Thus, with an increase in dimensions, evaluation becomes difficult. Function approximators such as neural networks handle this problem effectively. The issues with robotic systems are high dimensional states and actions because of anthropomorphic (human-like) robots. Classical reinforcement learning approaches consider a grid-world ...