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Within the part *Off-policy Monte Carlo Management *of the guide *Reinforcement Studying: An Introduction 2nd Version (web page 112)*, the writer left us with an fascinating train: utilizing the weighted significance sampling off-policy Monte Carlo technique to search out the quickest approach driving on each tracks. This train is complete that asks us to think about and construct nearly each part of a reinforcement studying activity, just like the atmosphere, agent, reward, actions, situations of termination, and the algorithm. Fixing this train is enjoyable and helps us construct a stable understanding of the interplay between algorithm and atmosphere, the significance of an accurate episodic activity definition, and the way the worth initialization impacts the coaching final result. By means of this publish, I hope to share my understanding and answer to this train with everybody inquisitive about reinforcement studying.

As talked about above, this train asks us to discover a coverage that makes a race automotive drive from the beginning line to the ending line as quick as attainable with out operating into gravel or off the observe. After rigorously studying the train descriptions, I listed some key factors which can be important to finish this activity:

**Map illustration**: maps on this context are literally 2D matrices with (row_index, column_index) as coordinates. The worth of every cell represents the state of that cell; as an illustration, we are able to use 0 to explain gravel, 1 for the observe floor, 0.4 for the beginning area, and 0.8 for the ending line. Any row and column index outdoors the matrix could be thought of as out-of-boundary.**Automobile illustration**: we are able to straight use the matrix’s coordinates to characterize the automotive’s place;**Velocity and management**: the rate house is discrete and consists of horizontal and vertical speeds that may be represented as a tuple (row_speed, col_speed). The velocity restrict on each axes is (-5, 5) and incremented by +1, 0, and -1 on every axis in every step; due to this fact, there are a complete of 9 attainable actions in every step. Actually, the velocity can’t be each zero besides on the beginning line, and the vertical velocity, or row velocity, can’t be unfavorable as we don’t need our automotive to drive again to the beginning line.**Reward and episode**: the reward for every step earlier than crossing the ending line is -1. When the automotive runs out of the observe, it’ll be reset to one of many beginning cells. The episode ends**ONLY**when the automotive efficiently crosses the ending line.**Beginning states**: we randomly select beginning cell for the automotive from the beginning line; the automotive’s preliminary velocity is (0, 0) in line with the train’s description.**Zero-acceleration problem**: the writer proposes a small*zero-acceleration problem*that, at every time step, with 0.1 likelihood, the motion is not going to take impact and the automotive will stay at its earlier velocity. We will implement this problem in coaching as an alternative of including the function to the atmosphere.

The answer to the train is separated into two posts; on this publish, we’ll give attention to constructing a racetrack atmosphere. The file construction of this train is as follows:

`|-- race_track_env`

| |-- maps

| | |-- build_tracks.py // this file is used to generate observe maps

| | |-- track_a.npy // observe a knowledge

| | |-- track_b.npy // observe b information

| |-- race_track.py // race observe atmosphere

|-- exercise_5_12_racetrack.py // the answer to this train

And the libraries used on this implementation are as follows:

`python==3.9.16`

numpy==1.24.3

matplotlib==3.7.1

pygame==2.5.0

We will characterize observe maps as 2D matrices with totally different values indicating observe states. I wish to be loyal to the train, so I’m attempting to construct the identical maps proven within the guide by assigning matrix values manually. The maps will likely be saved as separate *.npy* information in order that the atmosphere can learn the file in coaching as an alternative of producing them within the runtime.

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