In the not-so-distant future, taxicab companies across the World no longer employ human drivers to operate their fleet of vehicles. Instead, the taxicabs are operated by self-driving agents, known as smartcabs, to transport people from one location to another within the cities those companies operate.
In major metropolitan areas, such as Chicago, New York City, and San Francisco, an increasing number of people have come to depend on smartcabs to get to where they need to go as safely and reliably as possible. Although smartcabs have become the transport of choice, concerns have arose that a self-driving agent might not be as safe or reliable as human drivers, particularly when considering city traffic lights and other vehicles.
To alleviate these concerns, the task as an employee for a hypothetical National Taxicab Company is to use Reinforcement learning techniques to construct a demonstration of a smartcab operating in real-time to prove that both safety and reliability can be achieved.
Results for improved Q-Learning simulation:
In this project Reinforcement learning techniques applied for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time.
Milestones of the Project:
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Investigated the environment the agent operates in by constructing a very basic driving implementation.
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Identified each possible state the agent can be in when considering such things as traffic lights and oncoming traffic at each intersection.
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Implemented a Q-Learning algorithm for the self-driving agent to guide the agent towards its destination within the allotted time.
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Improved upon the Q-Learning algorithm to find the best configuration of learning and exploration factors to ensure the self-driving agent is reaching its destinations with consistently positive results.
This project uses the following software and Python libraries:
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer. pygame can then be installed using one of the following commands:
Mac: conda install -c https://conda.anaconda.org/quasiben pygame
Windows: conda install -c https://conda.anaconda.org/prkrekel pygame
Linux: conda install -c https://conda.anaconda.org/tlatorre pygame
The PyGame library can in some cases require a bit of troubleshooting to work correctly for this project. If you encounter an issue with PyGame, first see these helpful links below that are developed by communities of users working with the library:
This project contains three directories:
/logs/: This folder contains all log files that are given from the simulation when specific prerequisites are met./images/: This folder contains various images of cars to be used in the graphical user interface./smartcab/: This folder contains the Python scripts that create the environment, graphical user interface, the simulation, and the agents.
It also contains two files:
smartcab.ipynb: This is the main file that contains answers for questions and an analysis for work. -visuals.py: This Python script provides supplementary visualizations for the analysis.
Finally, in /smartcab/ are the following four files:
- Modify:
agent.py: This is the main Python file for the project.
- Do not modify:
environment.py: This Python file will create the smartcab environment.planner.py: This Python file creates a high-level planner for the agent to follow towards a set goal.simulation.py: This Python file creates the simulation and graphical user interface.
In a terminal or command window, navigate to the top-level project directory smartcab/ (that contains the two project directories) and run one of the following commands:
python smartcab/agent.py or
python -m smartcab.agent
This will run the agent.py file and execute implemented agent code into the environment. Additionally, use the command jupyter notebook smartcab.ipynb from this same directory to open up a browser window or tab to work with analysis notebook. Alternatively, you can use the command jupyter notebook or ipython notebook and navigate to the notebook file in the browser window that opens.
The smartcab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. U.S. Right-of-Way rules apply:
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On a green light, a left turn is permitted if there is no oncoming traffic making a right turn or coming straight through the intersection.
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On a red light, a right turn is permitted if no oncoming traffic is approaching from your left through the intersection.
To understand how to correctly yield to oncoming traffic when turning left, you may refer to this official drivers? education video, or this passionate exposition.
Assume that the smartcab is assigned a route plan based on the passengers, starting location and destination. The route is split at each intersection into waypoints, and you may assume that the smartcab, at any instant, is at some intersection in the world. Therefore, the next waypoint to the destination, assuming the destination has not already been reached, is one intersection away in one direction (North, South, East, or West).
The smartcab has only an egocentric view of the intersection it is at: It can determine the state of the traffic light for its direction of movement, and whether there is a vehicle at the intersection for each of the oncoming directions. For each action, the smartcab may either idle at the intersection, or drive to the next intersection to the left, right, or ahead of it. Finally, each trip has a time to reach the destination which decreases for each action taken (the passengers want to get there quickly). If the allotted time becomes zero before reaching the destination, the trip has failed.
The smartcab will receive positive or negative rewards based on the action it as taken. Expectedly, the smartcab will receive a small positive reward when making a good action, and a varying amount of negative reward dependent on the severity of the traffic violation it would have committed.
Based on the rewards and penalties the smartcab receives, the self-driving agent implementation should learn an optimal policy for driving on the city roads while obeying traffic rules, avoiding accidents, and reaching passengers? destinations in the allotted time.