Hands-On Reinforcement Learning with R by Giuseppe Ciaburro

Author:Giuseppe Ciaburro [Giuseppe Ciaburro]
Language: eng
Format: epub
Tags: COM004000 - COMPUTERS / Intelligence (AI) and Semantics, COM016000 - COMPUTERS / Computer Vision and Pattern Recognition, COM018000 - COMPUTERS / Data Processing
Publisher: Packt Publishing
Published: 2019-12-17T10:29:14+00:00

The following code block shows pseudo-code for the Q-learning algorithm:

Initialize

arbitrary action-value function

Repeat (for each episode)

Initialize s

choose a from s using policy from action-value function

Repeat (for each step in episode)

take action a

observe r, s'

update action-value function

update s

Q-learning uses a table to store each state-action couple. At each step, the agent observes the current state of the environment and using the π policy selects and executes the action. By executing the action, the agent obtains the reward, 𝑅𝑡+1, and the new state, 𝑆𝑡+1. At this point, the agent can calculate 𝑄 (s𝑡, a𝑡), updating the estimate.

In the following section, the basis of graph theory will be given and how this technology can be addressed in R.

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