The anatomy of the agent
As you learned in the previous chapter, there are several fundamental concepts in RL:
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The agent: A thing, or person, that takes an active role. In practice, the agent is some piece of code that implements some policy. Basically, this policy decides what action is needed at every time step, given our observations.
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The environment: Everything that is external to the agent and has the responsibility of providing observations and giving rewards. The environment changes its state based on the agent’s actions.
Let’s explore how both can be implemented in Python for a simple situation. We will define an environment that will give the agent random rewards for a limited number of steps, regardless of the agent’s actions. This scenario is not very useful in the real world, but it will allow us to focus on specific methods in both the environment and agent classes.
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