RL_APS

This repository is a development environment for my thesis which is "Reinforcement learning based automated parking systems". It provides the training envrionment for the agent in both parallel and perpendicular parkings.

The following videos present the trained agent in the parking simulation. The environment is a continuous action space in the perpendicular parking.

• without guidance

agent.mp4

• with guidance

agent.with.the.guidance.mp4

Folder structure

• sim_env: contains the necessary scripts for the parking simulation
  • parking_env.py: environment class
  • car.py: car class
  • com_fcn.py: common functions
  • parameters.py: parameters for the simulation environment
  • init_state.py: initialize the car/parking lot location and heading angle for the training
• training: contains the script for the training
  • training.py: for the training
  • utility.py: useful functions for the training
• practice_rllib: to learn how to use Ray RLlib for my thesis
• practice_pygame: to learn how to draw the parking environment for my thesis

Simulation Environment

used tools

The libraries and their versions are as follows.

tool	version
Python	3.10.11
Gymnasium	0.28.1
Ray RLlib	2.9.0
Numpy	1.26.3
Pygame	2.1.3

environment description

This environment equips both parallel and perpendicular parkings with both discrete and continuous action spaces.
As illustrated in the figure below, the custom environment can render a 2D environment with a top-down view and simulate parking movement using front-wheel steering through the Kinematic bicycle model. At each step, the car is rendered based on the input actions, which include acceleration and steering angle, and the next state of the vehicle is simulated.
The yellow rectangles represent the parking lot for two obstacles depicted as grey rectangles. The red rectangle indicates the parking lot for the agent, shown as the green rectangle. The grey grid line is drawn at 1-meter intervals. Although the visualization window size is 800 by 600 pixels, this corresponds to a physical size of 40 meters by 30 meters, defining 1 pixel as 0.05 meters and units are unified as meters. The parking lot size is 6 meters in length and 4 meters in width, while the car size for the agent is 4 meters in length and 2 meters in width. Furthermore, it can display some information such as the center of the car, the velocity, the heading angle and its radian. This is useful for debugging to make sure the car’s movement.

・Perpendicular parking image

・Parallel parking image

Kinematic Bicycle model

• Kinematic bicycle model equation
  In order to simulate the car’s movement, the kinematic bicycle model was used in the environment. (x, y) are the coordinates of the center of the car. The car’s velocity is controlled within 10 km/m since the car is usually at a low speed during parking. This means if the velocity becomes over 10 km/m, it is clipped as 10 km/m. The following equation is the Kinematic bicycle model used in the simulation.

    x_dot = v * cos(ψ)
    y_dot = v * sin(ψ)
    v_dot = a
    ψ_dot = v * tan(delta) / CAR_L
  

  v: velocity, ψ(psi): heading angle of the car, a: acceleration, δ(delta): steering angle, CAR_L: car length
  x: the center of the car position in x axle, y: the center of the car position in y axle

  The steering angle is limited between −𝜋/4 and 𝜋/4 to make sure the car's actions are realistic and to prevent the agent's exploration range from being too large. Therefore, in a given time, according to the equations above, the coordinate of the center of the car (𝑥′, 𝑦′) after moving can be obtained by the following equations.

  x_dash = dt ∗ x_dot + x  
  y_dash = dt ∗ y_dot + y  
  v = v_dot + v  
  ψ = dt ∗ ψ_dot  
  

Reinforcement learning

Environment

The overview of RL model for this project is illustrated in the following figure.

Action type

There are two action types which are continuous and discrete.
Actions as input values for the agent are [a, δ] in both type.
a is acceleration, limited between -1 and 1 m/s^2 as the maximum value. δ is set to between -𝜋/4〜𝜋/4 as the maximum value.

• continuous The agent can choose between -1 and 1 for both acceleration and steering angle, multiplying them by the maximum limit values.

• discrete There are 6 different actions.

  number	action values[𝑎, δ]	description
  0	[1, 0]	move forward
  1	[1, -𝜋/6]	move right forward
  2	[1, 𝜋/6]	move left forward
  3	[-1, 0]	move backward
  4	[-1, -𝜋/6]	move right backward
  5	[-1, 𝜋/6]	move left backward

State value for the agent

This section describes the state value designed in this project.
The coordinate of the parking space corner points, which means the transformed coordinate system from the global coordinate system to the local coordinate system. In global coordinates, the car must account for its own position and orientation within the global frame, complicating calculations. Expressing a global coordinate system as a local coordinate system simplifies the representation, making it easier to manage and understand. The following figure illustrates the local coordinate system where the front side of the car is positive along the y-axis, and the right side of the car is positive along the x-axis. The distance between each parking lot vertex and the center of the car is transformed into the car’s relative coordinate system.

The disance is divided by the maximaum distance for normalization and the Maximum distance is 25m.

Reward type

The following reward functions are desingned in this project. The reward is given at the end of each episode. Therefore, the current episode is terminated when one of these events happens.
Successful reward:
When the agent reverses into the parking lot, then the agent obtains a +1 reward.
Collision check:
When the agent enters the grey rectangle, the agent obtains a -1 reward.
Maximum step:
When the agent takes more than the set maximum step, it obtains a -1 reward.
Line cross-check:
When the agent crosses the parking lot border vertically or horizontally, it obtains a -1 reward.
This check is implemented to ensure realistic behavior, preventing the car from crossing to the opposite side of the parking lot to park. It is dependent on the placement of the parking lot. If the agent crosses the bottom border in Figure 10, it indicates a horizontal border crossing. Another example is if the parking lot is placed on the right side of the visualization window; when the agent crosses the right border of the parking lot, it indicates a vertical border crossing.
Maximum distance:
When the agent is farther away from the parking lot than the set maximum distance, it obtains a -1 reward.

guidance point

The agent received a +1 reward when it parked in the parking lot. However, the reward system was modified with the introduction of the guidance point.
If the car’s vertices are within the parking lot and the center of the car is within a set distance threshold from the center of the parking lot, the agent receives a +1 reward. A small value is then subtracted from this reward based on the angle error to ensure the car is parallel with the parking lot borders. The penalty for angle error is linearly related, where an angle error of 0 degrees results in no penalty, and larger errors reduce the reward accordingly, up to a maximum penalty of 0.5.
The reason for this is to encourage the agent to park near the center of the parking lot and to be parallel to the borders.
You can see the difference agent behaviour whether the guidance is used or not in 2 videos in Chapter RL_APS above.

How to use

instal tools

The first is to install necessary libraries.
ray rllib: pip install "ray[rllib]" tensorflow
Gymnasium: pip install "gymnasium[all]"

parameter settings

You can modify the maximum velocity, steps, acceleration, steering angle, car size, parking size and so on related to the simulation in parameters.py script.

Training

In the training.py, you can choose the parking type and action space type.
It is recommneded to set "no_render" as "render_mode" for the training in terms of efficiency and speed.

You can set the number of iterations for the training at line 17, num_train = "the number of iterations".

After these settings, you can execute the training.py script. After the training, the result folder and the agent folder are saved in the training folder.

• folder structure for the training
  training
  • parking type
    • action space
      • trained_agent: the policy data is stored.
      • training_result: the training result is stored.

Visualize the agent

After the training, you can observe the trained agent behaviour using, main.py script.
Line 12, set the folder name in the trained_agent folder saved after the training.
Do not forget to set "human" as "render_mode" at line 6.

Evaluation

Use the following command so that you can see data in the training_agent folder.
tensorboard --logdir=folder path
tensorboard --logdir=C:\Users-----------

For example: