Appendix

1. Introduction to Reinforcement Learning

Activity 1.01: Measuring the Performance of a Random Agent

  1. Import the required libraries – abc, numpy, and gym:

    import abc

    import numpy as np

    import gym

  2. Define the abstract class representing the agent:

    """

    Abstract class representing the agent

    Init with the action space and the function pi returning the action

    """

    class Agent:

        def __init__(self, action_space: gym.spaces.Space):

            """

            Constructor of the agent class.

            Args:

                action_space (gym.spaces.Space): environment action space

            """

            raise NotImplementedError("This class cannot be instantiated.")

        @abc.abstractmethod

        def pi(self, state: np.ndarray) -> np.ndarray:

            """

            Agent's policy. ...

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