← Machine Learning Projects

Machine Learning Engineer Nanodegree

Reinforcement Learning

Project: Train a Smartcab to Drive

Welcome to the fourth project of the Machine Learning Engineer Nanodegree! In this notebook, template code has already been provided for you to aid in your analysis of the Smartcab and your implemented learning algorithm. You will not need to modify the included code beyond what is requested. There will be questions that you must answer which relate to the project and the visualizations provided in the notebook. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide in agent.py.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.

Getting Started

In this project, you will work towards constructing an optimized Q-Learning driving agent that will navigate a Smartcab through its environment towards a goal. Since the Smartcab is expected to drive passengers from one location to another, the driving agent will be evaluated on two very important metrics: Safety and Reliability. A driving agent that gets the Smartcab to its destination while running red lights or narrowly avoiding accidents would be considered unsafe. Similarly, a driving agent that frequently fails to reach the destination in time would be considered unreliable. Maximizing the driving agent's safety and reliability would ensure that Smartcabs have a permanent place in the transportation industry.

Safety and Reliability are measured using a letter-grade system as follows:

Grade Safety Reliability
A+ Agent commits no traffic violations,
and always chooses the correct action.		 Agent reaches the destination in time
for 100% of trips.
A Agent commits few minor traffic violations,
such as failing to move on a green light.		 Agent reaches the destination on time
for at least 90% of trips.
B Agent commits frequent minor traffic violations,
such as failing to move on a green light.		 Agent reaches the destination on time
for at least 80% of trips.
C Agent commits at least one major traffic violation,
such as driving through a red light.		 Agent reaches the destination on time
for at least 70% of trips.
D Agent causes at least one minor accident,
such as turning left on green with oncoming traffic.		 Agent reaches the destination on time
for at least 60% of trips.
F Agent causes at least one major accident,
such as driving through a red light with cross-traffic.		 Agent fails to reach the destination on time
for at least 60% of trips.

To assist evaluating these important metrics, you will need to load visualization code that will be used later on in the project. Run the code cell below to import this code which is required for your analysis.

In [3]:
# Import the visualization code
import visuals as vs

# Pretty display for notebooks
%matplotlib inline

Understand the World

Before starting to work on implementing your driving agent, it's necessary to first understand the world (environment) which the Smartcab and driving agent work in. One of the major components to building a self-learning agent is understanding the characteristics about the agent, which includes how the agent operates. To begin, simply run the agent.py agent code exactly how it is -- no need to make any additions whatsoever. Let the resulting simulation run for some time to see the various working components. Note that in the visual simulation (if enabled), the white vehicle is the Smartcab.

Question 1

In a few sentences, describe what you observe during the simulation when running the default agent.py agent code. Some things you could consider:

  • Does the Smartcab move at all during the simulation?
  • What kind of rewards is the driving agent receiving?
  • How does the light changing color affect the rewards?

Hint: From the /smartcab/ top-level directory (where this notebook is located), run the command

'python smartcab/agent.py'

Answer:

The smartcab doesn't appear to move at all during the simulation - it either successfully idles at a red light, successfully idles at a green light when there's oncoming traffic, or mistakenly idles at a green light when there's no oncoming traffic. It begins by receiving rewards of around +2 to +3 pts for correctly idling at the red light, gaining around 0 to +2 pts for correctly idling at green lights with oncoming traffic, and losing around -4 to -6 pts for idling incorrectly at a green light. The light changing color affects the signs of the rewards dependent on whether there is oncoming traffic or not, as is evident above.

Understand the Code

In addition to understanding the world, it is also necessary to understand the code itself that governs how the world, simulation, and so on operate. Attempting to create a driving agent would be difficult without having at least explored the "hidden" devices that make everything work. In the /smartcab/ top-level directory, there are two folders: /logs/ (which will be used later) and /smartcab/. Open the /smartcab/ folder and explore each Python file included, then answer the following question.

Question 2

  • In the agent.py Python file, choose three flags that can be set and explain how they change the simulation.
  • In the environment.py Python file, what Environment class function is called when an agent performs an action?
  • In the simulator.py Python file, what is the difference between the 'render_text()' function and the 'render()' function?
  • In the planner.py Python file, will the 'next_waypoint() function consider the North-South or East-West direction first?

Answer:

  • In the agent.py file, the flag 'grid_size' alters the number of intersections (effectively the size of the map) that the car can make decisions in; the flag 'log_metrics' must be set to true for the trials and simulation results to be stored for analysis in the /logs folder; the flag 'epsilon' allows us to control the degree to which the algorithm explores its environment by taking random actions to discover new state-action rewards.

  • In the environment.py file, the Environment class function 'act' which takes the parameters self, agent, and action is called when an agent performs an action.

  • In the simulator.py file, 'render_text()' returns information about the simulation to the terminal window in text-form while 'render()' is a visually rendered display of the simulation.

  • In the planner.py file, the 'next_waypoint()' function will consider the East-West direction first and then check the North-South direction.

Implement a Basic Driving Agent

The first step to creating an optimized Q-Learning driving agent is getting the agent to actually take valid actions. In this case, a valid action is one of None, (do nothing) 'Left' (turn left), 'Right' (turn right), or 'Forward' (go forward). For your first implementation, navigate to the 'choose_action()' agent function and make the driving agent randomly choose one of these actions. Note that you have access to several class variables that will help you write this functionality, such as 'self.learning' and 'self.valid_actions'. Once implemented, run the agent file and simulation briefly to confirm that your driving agent is taking a random action each time step.

Basic Agent Simulation Results

To obtain results from the initial simulation, you will need to adjust following flags:

  • 'enforce_deadline' - Set this to True to force the driving agent to capture whether it reaches the destination in time.
  • 'update_delay' - Set this to a small value (such as 0.01) to reduce the time between steps in each trial.
  • 'log_metrics' - Set this to True to log the simluation results as a .csv file in /logs/.
  • 'n_test' - Set this to '10' to perform 10 testing trials.

Optionally, you may disable to the visual simulation (which can make the trials go faster) by setting the 'display' flag to False. Flags that have been set here should be returned to their default setting when debugging. It is important that you understand what each flag does and how it affects the simulation!

Once you have successfully completed the initial simulation (there should have been 20 training trials and 10 testing trials), run the code cell below to visualize the results. Note that log files are overwritten when identical simulations are run, so be careful with what log file is being loaded! Run the agent.py file after setting the flags from projects/smartcab folder instead of projects/smartcab/smartcab.

In [4]:
# Load the 'sim_no-learning' log file from the initial simulation results
vs.plot_trials('sim_no-learning.csv')

Question 3

Using the visualization above that was produced from your initial simulation, provide an analysis and make several observations about the driving agent. Be sure that you are making at least one observation about each panel present in the visualization. Some things you could consider:

  • How frequently is the driving agent making bad decisions? How many of those bad decisions cause accidents?
  • Given that the agent is driving randomly, does the rate of reliabilty make sense?
  • What kind of rewards is the agent receiving for its actions? Do the rewards suggest it has been penalized heavily?
  • As the number of trials increases, does the outcome of results change significantly?
  • Would this Smartcab be considered safe and/or reliable for its passengers? Why or why not?

Answer:

The driving agent makes bad decisions around 40% of the time, and causes major accidents around 5% of the time and minor accidents between 2 and 3% of the time. The rate of reliability is 0% for 6 of the 10 test trials and 10% for 4 of the 10 test trials, which does make sense given the car is driving randomly. The agent receives negative rewards on average between -3 and -6, which does suggest that it has been penalized heavily as driving correctly would result in only positive rewards. The outcome of the results does not seem to change significantly over time; the discrepancies between each trial are small enough to be explained by random noise. This Smartcab would definitely be considered neither safe nor reliable for its passengers due to the high frequency of accidents and traffic violations (both minor and major), and because of its inability to get its passengers to the destination on time, which occurred 80% of the time. It deserves its ratings of F on both safety and reliability as in each trial it commmitted a major accident and failed to reach the destination on time for at least 60% of the trips.

Inform the Driving Agent

The second step to creating an optimized Q-learning driving agent is defining a set of states that the agent can occupy in the environment. Depending on the input, sensory data, and additional variables available to the driving agent, a set of states can be defined for the agent so that it can eventually learn what action it should take when occupying a state. The condition of 'if state then action' for each state is called a policy, and is ultimately what the driving agent is expected to learn. Without defining states, the driving agent would never understand which action is most optimal -- or even what environmental variables and conditions it cares about!

Identify States

Inspecting the 'build_state()' agent function shows that the driving agent is given the following data from the environment:

  • 'waypoint', which is the direction the Smartcab should drive leading to the destination, relative to the Smartcab's heading.
  • 'inputs', which is the sensor data from the Smartcab. It includes
    • 'light', the color of the light.
    • 'left', the intended direction of travel for a vehicle to the Smartcab's left. Returns None if no vehicle is present.
    • 'right', the intended direction of travel for a vehicle to the Smartcab's right. Returns None if no vehicle is present.
    • 'oncoming', the intended direction of travel for a vehicle across the intersection from the Smartcab. Returns None if no vehicle is present.
  • 'deadline', which is the number of actions remaining for the Smartcab to reach the destination before running out of time.

Question 4

Which features available to the agent are most relevant for learning both safety and efficiency? Why are these features appropriate for modeling the Smartcab in the environment? If you did not choose some features, why are those features not appropriate?

Answer:

The features most relevant for learning safety are 'light' and 'oncoming' as they inform the cab of the information necessary to avoid accidents and violations. The features most relevant for learning efficiency is 'waypoint' as it informs the cab which direction to head in. These features are appropriate for modelling the smartcab environment because they inform the car of the base information needed to directly avoid making mistakes and for arriving at the destination on time; in contrast, an example of a feature of the smartcab environment that could indirectly affect the smartcab's ability to learn safety and efficiency is whether or not it is raining, as this could alter the behaviour of other cars on the road (driving slower / more likely to get into accidents), though in our simple model the behaviour of other cars is not taken into account. I chose not to select 'right' and 'left' as while this information is important for having a better understanding of the environment, I did not think it would have as significant an impact on the smartcab's ability to learn safety and efficiency as the other features, since only 'left' could directly affect the number of accidents it got into, violations it accrued, or the length of time it would take to reach the destination in the case where the smartcab wished to turn right at a red light. I also chose to omit 'deadline' because it may not make sense for the smartcab to even have to worry about the number of actions remaining, since it should always be making the optimal decision to move towards the destination anyway, and the number of actions remaining should not alter its behaviour.

In conclusion, our state space will contain only 3 features: 'waypoint', 'light', and 'oncoming'.

Define a State Space

When defining a set of states that the agent can occupy, it is necessary to consider the size of the state space. That is to say, if you expect the driving agent to learn a policy for each state, you would need to have an optimal action for every state the agent can occupy. If the number of all possible states is very large, it might be the case that the driving agent never learns what to do in some states, which can lead to uninformed decisions. For example, consider a case where the following features are used to define the state of the Smartcab:

('is_raining', 'is_foggy', 'is_red_light', 'turn_left', 'no_traffic', 'previous_turn_left', 'time_of_day').

How frequently would the agent occupy a state like (False, True, True, True, False, False, '3AM')? Without a near-infinite amount of time for training, it's doubtful the agent would ever learn the proper action!

Question 5

If a state is defined using the features you've selected from Question 4, what would be the size of the state space? Given what you know about the evironment and how it is simulated, do you think the driving agent could learn a policy for each possible state within a reasonable number of training trials?
Hint: Consider the combinations of features to calculate the total number of states!

Answer:

The state space would contain 3 features: ('waypoint', 'light', and 'oncoming'). 'Waypoint' could take 3 different values (Forward, Left, and Right). 'Light' could hypothetically take three values (Red, Amber, Green), though in this simple model it seems like the only two options are either Red or Green. 'Oncoming' could take four values (None, Forward, Right, Left) because it defines the direction of the oncoming traffic or it says that there is no oncoming traffic.

Let's start by calculating the possible number of states by multiplying the number of states together, so 3 x 2 x 4 = 24. This appears manageable and it could be possible to learn a policy for each possible state within a reasonable number of training trials.

Update the Driving Agent State

For your second implementation, navigate to the 'build_state()' agent function. With the justification you've provided in Question 4, you will now set the 'state' variable to a tuple of all the features necessary for Q-Learning. Confirm your driving agent is updating its state by running the agent file and simulation briefly and note whether the state is displaying. If the visual simulation is used, confirm that the updated state corresponds with what is seen in the simulation.

Note: Remember to reset simulation flags to their default setting when making this observation!

Implement a Q-Learning Driving Agent

The third step to creating an optimized Q-Learning agent is to begin implementing the functionality of Q-Learning itself. The concept of Q-Learning is fairly straightforward: For every state the agent visits, create an entry in the Q-table for all state-action pairs available. Then, when the agent encounters a state and performs an action, update the Q-value associated with that state-action pair based on the reward received and the interative update rule implemented. Of course, additional benefits come from Q-Learning, such that we can have the agent choose the best action for each state based on the Q-values of each state-action pair possible. For this project, you will be implementing a decaying, $\epsilon$-greedy Q-learning algorithm with no discount factor. Follow the implementation instructions under each TODO in the agent functions.

Note that the agent attribute self.Q is a dictionary: This is how the Q-table will be formed. Each state will be a key of the self.Q dictionary, and each value will then be another dictionary that holds the action and Q-value. Here is an example:

{ 'state-1': { 
    'action-1' : Qvalue-1,
    'action-2' : Qvalue-2,
     ...
   },
  'state-2': {
    'action-1' : Qvalue-1,
     ...
   },
   ...
}

Furthermore, note that you are expected to use a decaying $\epsilon$ (exploration) factor. Hence, as the number of trials increases, $\epsilon$ should decrease towards 0. This is because the agent is expected to learn from its behavior and begin acting on its learned behavior. Additionally, the agent will be tested on what it has learned after $\epsilon$ has passed a certain threshold (the default threshold is 0.01). For the initial Q-Learning implementation, you will be implementing a linear decaying function for $\epsilon$.

Q-Learning Simulation Results

To obtain results from the initial Q-Learning implementation, you will need to adjust the following flags and setup:

  • 'enforce_deadline' - Set this to True to force the driving agent to capture whether it reaches the destination in time.
  • 'update_delay' - Set this to a small value (such as 0.01) to reduce the time between steps in each trial.
  • 'log_metrics' - Set this to True to log the simluation results as a .csv file and the Q-table as a .txt file in /logs/.
  • 'n_test' - Set this to '10' to perform 10 testing trials.
  • 'learning' - Set this to 'True' to tell the driving agent to use your Q-Learning implementation.

In addition, use the following decay function for $\epsilon$:

$$ \epsilon_{t+1} = \epsilon_{t} - 0.05, \hspace{10px}\textrm{for trial number } t$$

If you have difficulty getting your implementation to work, try setting the 'verbose' flag to True to help debug. Flags that have been set here should be returned to their default setting when debugging. It is important that you understand what each flag does and how it affects the simulation!

Once you have successfully completed the initial Q-Learning simulation, run the code cell below to visualize the results. Note that log files are overwritten when identical simulations are run, so be careful with what log file is being loaded!

In [48]:
# Load the 'sim_default-learning' file from the default Q-Learning simulation
vs.plot_trials('sim_default-learning.csv')

Question 6

Using the visualization above that was produced from your default Q-Learning simulation, provide an analysis and make observations about the driving agent like in Question 3. Note that the simulation should have also produced the Q-table in a text file which can help you make observations about the agent's learning. Some additional things you could consider:

  • Are there any observations that are similar between the basic driving agent and the default Q-Learning agent?
  • Approximately how many training trials did the driving agent require before testing? Does that number make sense given the epsilon-tolerance?
  • Is the decaying function you implemented for $\epsilon$ (the exploration factor) accurately represented in the parameters panel?
  • As the number of training trials increased, did the number of bad actions decrease? Did the average reward increase?
  • How does the safety and reliability rating compare to the initial driving agent?

Answer:

The smartcab is making bad decisions around 15% of the time which is significantly less than 40% of the time, and causing major accidents around 4% of the time and minor accidents around 1.5% of the time.
The rolling rate of reliability has approved from hovering mostly around 0% to improving to around 70%, and the rolling average reward per action remained at around increased from -5 to -1.
The agent required around 20 trials before testing, which makes sense given that the epsilon-tolerance formula is epsilon(t+1) = epsilon(t) - 0.05, beginning with epsilon = 1.0. This decaying function for epsilon is accurately represented in the parameters panel, as it is a linear function that has a slope of 1 and negative intercept of -0.05.
As the number of trials increased, the number of bad actions decreased and average reward increased, which shows that the agent was successfully learning better state-action pairs.
The Safety and Reliability Ratings improved remarkably from Fs to A+s, which almost seems too good to be true given that the criteria for an A+ on Safety is an agent comitting no traffic violations and always choosing the correct action, and an A+ on reliability is an agent that reaches the destination in time for 100% of trips, while our visualisations seem to indicate that our agent is still getting into accidents and not reaching the destination in time for 100% of trips.

Improve the Q-Learning Driving Agent

The third step to creating an optimized Q-Learning agent is to perform the optimization! Now that the Q-Learning algorithm is implemented and the driving agent is successfully learning, it's necessary to tune settings and adjust learning paramaters so the driving agent learns both safety and efficiency. Typically this step will require a lot of trial and error, as some settings will invariably make the learning worse. One thing to keep in mind is the act of learning itself and the time that this takes: In theory, we could allow the agent to learn for an incredibly long amount of time; however, another goal of Q-Learning is to transition from experimenting with unlearned behavior to acting on learned behavior. For example, always allowing the agent to perform a random action during training (if $\epsilon = 1$ and never decays) will certainly make it learn, but never let it act. When improving on your Q-Learning implementation, consider the impliciations it creates and whether it is logistically sensible to make a particular adjustment.

Improved Q-Learning Simulation Results

To obtain results from the initial Q-Learning implementation, you will need to adjust the following flags and setup:

  • 'enforce_deadline' - Set this to True to force the driving agent to capture whether it reaches the destination in time.
  • 'update_delay' - Set this to a small value (such as 0.01) to reduce the time between steps in each trial.
  • 'log_metrics' - Set this to True to log the simluation results as a .csv file and the Q-table as a .txt file in /logs/.
  • 'learning' - Set this to 'True' to tell the driving agent to use your Q-Learning implementation.
  • 'optimized' - Set this to 'True' to tell the driving agent you are performing an optimized version of the Q-Learning implementation.

Additional flags that can be adjusted as part of optimizing the Q-Learning agent:

  • 'n_test' - Set this to some positive number (previously 10) to perform that many testing trials.
  • 'alpha' - Set this to a real number between 0 - 1 to adjust the learning rate of the Q-Learning algorithm.
  • 'epsilon' - Set this to a real number between 0 - 1 to adjust the starting exploration factor of the Q-Learning algorithm.
  • 'tolerance' - set this to some small value larger than 0 (default was 0.05) to set the epsilon threshold for testing.

Furthermore, use a decaying function of your choice for $\epsilon$ (the exploration factor). Note that whichever function you use, it must decay to 'tolerance' at a reasonable rate. The Q-Learning agent will not begin testing until this occurs. Some example decaying functions (for $t$, the number of trials):

$$ \epsilon = a^t, \textrm{for } 0 < a < 1 \hspace{50px}\epsilon = \frac{1}{t^2}\hspace{50px}\epsilon = e^{-at}, \textrm{for } 0 < a < 1 \hspace{50px} \epsilon = \cos(at), \textrm{for } 0 < a < 1$$

You may also use a decaying function for $\alpha$ (the learning rate) if you so choose, however this is typically less common. If you do so, be sure that it adheres to the inequality $0 \leq \alpha \leq 1$.

If you have difficulty getting your implementation to work, try setting the 'verbose' flag to True to help debug. Flags that have been set here should be returned to their default setting when debugging. It is important that you understand what each flag does and how it affects the simulation!

Once you have successfully completed the improved Q-Learning simulation, run the code cell below to visualize the results. Note that log files are overwritten when identical simulations are run, so be careful with what log file is being loaded!

In [51]:
# Load the 'sim_improved-learning' file from the improved Q-Learning simulation
vs.plot_trials('sim_improved-learning.csv')

Question 7

Using the visualization above that was produced from your improved Q-Learning simulation, provide a final analysis and make observations about the improved driving agent like in Question 6. Questions you should answer:

  • What decaying function was used for epsilon (the exploration factor)?
  • Approximately how many training trials were needed for your agent before beginning testing?
  • What epsilon-tolerance and alpha (learning rate) did you use? Why did you use them?
  • How much improvement was made with this Q-Learner when compared to the default Q-Learner from the previous section?
  • Would you say that the Q-Learner results show that your driving agent successfully learned an appropriate policy?
  • Are you satisfied with the safety and reliability ratings of the Smartcab?

Answer:

I unexpectedly found that all of the changes I made caused the agent to perform worse than before. I hypothesised that part of the reason it was returning such high scores despite having only 20 training trials was due to the low number of possible states I initialised, so I changed:

state = waypoint,inputs['light'],inputs['oncoming']
to
state = waypoint,inputs['light'],inputs['oncoming'],inputs['left'],inputs['right']

and altered the epsilon function. I experimented by changing the epsilon function to epsilon = 1 / t^2, epsilon = epsilon - 0.005, and epsilon = epsilon - 0.001. I also played around with the learning rate, changing it to 0.8, 0.6, and 0.4, and found that all iterations in combination with the changed epsilon function still performed worse than the simpler model with fewer states. For my final model I settled on using the original decay function of epsilon = epsilon - 0.05, which required 20 training trials. I stuck with an epsilon-tolerance of 0 and learning rate of 0.5 since they worked consistently well before. Since the default Q-Learner turned out to be the best of the Q-Learners I tried, there was no change between the default and final Q-Learners. I would say that the Q-Learner results show that the driving agent successfully learned an appropriate policy as demonstrated by the safety and reliability ratings of the Smartcab being A+s.

Define an Optimal Policy

Sometimes, the answer to the important question "what am I trying to get my agent to learn?" only has a theoretical answer and cannot be concretely described. Here, however, you can concretely define what it is the agent is trying to learn, and that is the U.S. right-of-way traffic laws. Since these laws are known information, you can further define, for each state the Smartcab is occupying, the optimal action for the driving agent based on these laws. In that case, we call the set of optimal state-action pairs an optimal policy. Hence, unlike some theoretical answers, it is clear whether the agent is acting "incorrectly" not only by the reward (penalty) it receives, but also by pure observation. If the agent drives through a red light, we both see it receive a negative reward but also know that it is not the correct behavior. This can be used to your advantage for verifying whether the policy your driving agent has learned is the correct one, or if it is a suboptimal policy.

Question 8

Provide a few examples (using the states you've defined) of what an optimal policy for this problem would look like. Afterwards, investigate the 'sim_improved-learning.txt' text file to see the results of your improved Q-Learning algorithm. For each state that has been recorded from the simulation, is the policy (the action with the highest value) correct for the given state? Are there any states where the policy is different than what would be expected from an optimal policy? Provide an example of a state and all state-action rewards recorded, and explain why it is the correct policy.

Answer:

An optimal policy for the states we've defined,
state = waypoint,inputs['light'],inputs['oncoming']
would be one where the smartcab waits at red lights, waits at green lights when there's oncoming traffic and it would like to turn left, moves forward when the light is green and there's no oncoming traffic, and makes decisions that move it in the correct direction of the waypoint.

In most cases, the policy appears to be correct for the given states; as one example, if the light is green and there's no oncoming traffic, the car responds appropriately by driving forward if the waypoint is also in the forward direction. An example of this optimal policy from the Q-Table would be:

('forward', 'green', None)
-- forward : 1.99
-- None : -2.43
-- right : 0.88
-- left : 0.50

where the choice it would make is to go forward.

An example of a sub-optimal policy from the Q-Table would be:

('right', 'green', 'forward') -- forward : 0.17 -- None : 0.00 -- right : 0.00 -- left : -10.45

where the car drives forward instead of right because it appears to have never learned a state-action reward pair for either going right or doing nothing in that situation. A way to fix this would be to increase our number of training trials so it would mean the car would be more likely to encounter the situation.

Optional: Future Rewards - Discount Factor, 'gamma'

Curiously, as part of the Q-Learning algorithm, you were asked to not use the discount factor, 'gamma' in the implementation. Including future rewards in the algorithm is used to aid in propogating positive rewards backwards from a future state to the current state. Essentially, if the driving agent is given the option to make several actions to arrive at different states, including future rewards will bias the agent towards states that could provide even more rewards. An example of this would be the driving agent moving towards a goal: With all actions and rewards equal, moving towards the goal would theoretically yield better rewards if there is an additional reward for reaching the goal. However, even though in this project, the driving agent is trying to reach a destination in the allotted time, including future rewards will not benefit the agent. In fact, if the agent were given many trials to learn, it could negatively affect Q-values!

Optional Question 9

There are two characteristics about the project that invalidate the use of future rewards in the Q-Learning algorithm. One characteristic has to do with the Smartcab itself, and the other has to do with the environment. Can you figure out what they are and why future rewards won't work for this project?

Answer:

Note: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to
File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.