October 2019
Intermediate to advanced
366 pages
12h 4m
English
Content preview from Reinforcement Learning Algorithms with Python
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Policy
The policy defines how the agent selects an action given a state. The policy chooses the action that maximizes the cumulative reward from that state, not with the bigger immediate reward. It takes care of looking for the long-term goal of the agent. For example, if a car has another 30 km to go before reaching its destination, but only has another 10 km of autonomy left and the next gas stations are 1 km and 60 km away, then the policy will choose to get fuel at the first gas station (1 km away) in order to not run out of gas. This decision is not optimal in the immediate future as it will take some time to refuel, but it will be sure to ultimately accomplish the goal.
The following diagram shows a simple example where an actor moving ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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