We also thank Daniel Urieli for the initial adaptation of this assignment for the CS343 Artificial Intelligence course at The University of Texas at Austin.
Pacman seeks reward.
Should he eat or should he run?
When in doubt, q-learn.
In this project, you will implement value iteration and q-learning. You will test your agents first on Gridworld (from class), then apply them to a simulated robot controller (Crawler) and Pacman.
The code for this project contains the following files, which are available in a zip archive:
||A value iteration agent for solving known MDPs.|
||Q-learning agents for Gridworld, Crawler and Pacman|
||A file to put your answers to questions given in the project.|
||Defines methods on general MDPs.|
||Defines the base classes
||The Gridworld implementation|
||Classes for extracting features on (state,action) pairs. Used for the approximate q-learning agent (in qlearningAgents.py).|
||Abstract class for general reinforcement learning environments. Used
||Gridworld graphical display.|
||Plug-in for the Gridworld text interface.|
||The crawler code and test harness. You will run this but not edit it.|
||GUI for the crawler robot.|
||GUI for the crawler robot.|
||Parses autograder test and solution files|
||General autograding test classes|
||Directory containing the test cases for each question|
||Project 3 specific autograding test classes|
What to submit: You will fill in portions of
analysis.py during the assignment. You should submit only these files. Please don't change any others.
Evaluation: Your code will be autograded for technical correctness. Please do not change the names of any provided functions or classes within the code, or you will wreak havoc on the autograder. However, the correctness of your implementation -- not the autograder's judgements -- will be the final judge of your score. If necessary, we will review and grade assignments individually to ensure that you receive due credit for your work.
Academic Dishonesty: We will be checking your code against other submissions in the class for logical redundancy. If you copy someone else's code and submit it with minor changes, we will know. These cheat detectors are quite hard to fool, so please don't try. We trust you all to submit your own work only; please don't let us down. If you do, we will pursue the strongest consequences available to us.
Getting Help: You are not alone! If you find yourself stuck on something, contact the course staff for help. Office hours, section, and the newsgroup are there for your support; please use them. If you can't make our office hours, let us know and we will schedule more. We want these projects to be rewarding and instructional, not frustrating and demoralizing. But, we don't know when or how to help unless you ask.
To get started, run Gridworld in manual control mode, which uses the arrow keys:
python gridworld.py -m
You will see the two-exit layout from class. The blue dot is the agent. Note that when you press up, the agent only actually moves north 80% of the time. Such is the life of a Gridworld agent!
You can control many aspects of the simulation. A full list of options is available by running:
python gridworld.py -h
The default agent moves randomly
python gridworld.py -g MazeGrid
You should see the random agent bounce around the grid until it happens upon an exit. Not the finest hour for an AI agent.
Note: The Gridworld MDP is such that you first must enter a pre-terminal state (the double boxes shown in the GUI) and then take the special 'exit' action before the episode actually ends (in the true terminal state called
TERMINAL_STATE, which is not shown in the GUI). If you run an episode manually, your total return may be less than you expected, due to the discount rate (
-d to change; 0.9 by default).
Look at the console output that accompanies the graphical output (or use
-t for all text).
You will be told about each transition the agent experiences (to turn this off, use
As in Pacman, positions are represented by
(x,y) Cartesian coordinates
and any arrays are indexed by
the direction of increasing
y, etc. By default,
most transitions will receive a reward of zero, though you can change this
with the living reward option (
Question 1 (6 points) Write a value iteration agent in
ValueIterationAgent, which has been partially specified for you in
valueIterationAgents.py. Your value iteration agent is an offline planner, not a reinforcement agent, and so the relevant training option is the number of iterations of value iteration it should run (option
-i) in its initial planning phase.
ValueIterationAgent takes an MDP on construction and runs value iteration for the specified number of iterations before the constructor returns.
Value iteration computes k-step estimates of the optimal values, Vk. In addition to running value iteration, implement the following methods for
ValueIterationAgent using Vk.
computeActionFromValues(state)computes the best action according to the value function given by
computeQValueFromValues(state, action)returns the Q-value of the (state, action) pair given by the value function given by
These quantities are all displayed in the GUI: values are numbers in squares, q-values are numbers in square quarters, and policies are arrows out from each square.
Important: Use the "batch" version of value iteration where each vector Vk is computed from a fixed vector Vk-1 (like in lecture), not the "online" version where one single weight vector is updated in place. The difference is discussed in Sutton & Barto in the 6th paragraph of chapter 4.1.
Note: A policy synthesized from values of depth k (which reflect the next k rewards) will actually reflect the next k+1 rewards (i.e. you return πk+1). Similarly, the q-values will also reflect one more reward than the values (i.e. you return Qk+1). You may assume that 100 iterations is enough for convergence in the questions below.
To test your implementation, run the autograder:
python autograder.py -q q1
The following command loads your
ValueIterationAgent, which will compute a policy and execute it 10 times. Press a key to cycle through values, q-values, and the simulation. You should find that the value of the start state (
V(start)) and the empirical resulting average reward are quite close.
python gridworld.py -a value -i 100 -k 10
Hint: On the default BookGrid, running value iteration for 5 iterations should give you this output:
python gridworld.py -a value -i 5
Your value iteration agent will be graded on a new grid. We will check your values, q-values, and policies after fixed numbers of iterations and at convergence (e.g. after 100 iterations).
Hint: Use the
util.Counter class in
which is a dictionary with a default value of zero. Methods such as
totalCount should simplify your code. However, be careful with
argMax: the actual argmax you want may be a key not in the counter!
Note: Make sure to handle the case when a state has no available actions in an MDP (think about what this means for future rewards).
Question 2 (1 point) On
BridgeGrid with the default discount of 0.9 and the default noise of 0.2, the optimal policy does not cross the bridge.
Change only ONE of the discount and noise parameters so that the optimal policy causes the agent to attempt to cross the bridge. Put your answer in
analysis.py. (Noise refers to how often an agent ends up in an unintended successor state when they perform an action.) The default corresponds to:
python gridworld.py -a value -i 100 -g BridgeGrid --discount 0.9 --noise 0.2
python autograder.py -q q2
Question 3 (5 points) On the
DiscountGrid, give an assignment of parameter values for discount, noise, and livingReward which produce the following optimal policy types or state that the policy is impossible by returning the string
'NOT POSSIBLE'. The default corresponds to:
python gridworld.py -a value -i 100 -g DiscountGrid --discount 0.9 --noise 0.2 --livingReward 0.0
To check your answers, run the autograder:
python autograder.py -q q3
question3e()should each return a 3-item tuple of (discount, noise, living reward) in
Note: You can check your policies in the GUI. For example, using a correct answer to 3(a), the arrow in (0,1) should point east, the arrow in (1,1) should also point east, and the arrow in (2,1) should point north.
Note: On some machines you may not see an arrow. In this case, press a button on the keyboard to switch to qValue display, and mentally calculate the policy by taking the arg max of the available qValues for each state.
Note that your value iteration agent does not actually learn from experience. Rather, it ponders its MDP model to arrive at a complete policy before ever interacting with a real environment. When it does interact with the environment, it simply follows the precomputed policy (e.g. it becomes a reflex agent). This distinction may be subtle in a simulated environment like a Gridword, but it's very important in the real world, where the real MDP is not available.
Question 4 (5 points) You will now write a q-learning agent, which does very little on construction, but instead learns by trial and error from interactions with the environment through its
update(state, action, nextState, reward) method. A stub of a q-learner is specified in
qlearningAgents.py, and you can select it with the option
'-a q'. For this question, you must implement the
getPolicy, you should break ties randomly for better behavior. The
random.choice() function will help. In a particular state, actions that your agent hasn't seen before still have a Q-value, specifically a Q-value of zero, and if all of the actions that your agent has seen before have a negative Q-value, an unseen action may be optimal.
With the q-learning update in place, you can watch your q-learner learn under manual control, using the keyboard:
python gridworld.py -a q -k 5 -mRecall that
-kwill control the number of episodes your agent gets to learn. Watch how the agent learns about the state it was just in, not the one it moves to, and "leaves learning in its wake."
Grading: We will run your Q-learning agent and check that it learns the same Q-values and policy as our reference implementation when each is presented with the same set of examples. To grade your implementation, run the autograder:
python autograder.py -q q4
Question 5a (3 points) Complete your q-learning agent by implementing epsilon-greedy action selection in
getAction, meaning it chooses random actions epsilon of the time, and follows its current best q-values otherwise.
python gridworld.py -a q -k 100Your final q-values should resemble those of your value iteration agent, especially along well-traveled paths. However, your average returns will be lower than the q-values predict because of the random actions and the initial learning phase.
You can choose an element from a list uniformly at random by calling the
You can simulate a binary variable with probability
of success by using
util.flipCoin(p), which returns
False with probability
With no additional code, you should now be able to run a q-learning crawler robot:
python crawler.pyIf this doesn't work, you've probably written some code too specific to the
GridWorldproblem and you should make it more general to all MDPs. You will receive full credit if the command above works without exceptions.
This will invoke the crawling robot from class using your q-learner. Play around with the various learning parameters to see how they affect the agent's policies and actions. Note that the step delay is a parameter of the simulation, whereas the learning rate and epsilon are parameters of your learning algorithm, and the discount factor is a property of the environment.
Question 6 (1 points) First, train a completely random q-learner with the default learning rate on the noiseless BridgeGrid for 50 episodes and observe whether it finds the optimal policy.
python gridworld.py -a q -k 50 -n 0 -g BridgeGrid -e 1Now try the same experiment with an epsilon of 0. Is there an epsilon and a learning rate for which it is highly likely (greater than 99%) that the optimal policy will be learned after 50 iterations?
question6()should return EITHER a 2-item tuple of
(epsilon, learning rate)OR the string
'NOT POSSIBLE'if there is none. Epsilon is controlled by
-e, learning rate by
You may run the autograder using the usual syntax.
Question 7 (1 points) Time to play some Pacman! Pacman will play games in two phases.
In the first phase, training, Pacman will begin to learn about the values of positions and actions.
Because it takes a very long time to learn accurate q-values even for tiny grids, Pacman's training games
run in quiet mode by default, with no GUI (or console) display. Once Pacman's training is complete,
he will enter testing mode. When testing, Pacman's
self.alpha will be set to 0.0, effectively stopping q-learning and disabling exploration, in order to allow Pacman to exploit his learned policy. Test games are shown in the GUI by default. Without any code changes you should be able to run q-learning Pacman for very tiny grids as follows:
python pacman.py -p PacmanQAgent -x 2000 -n 2010 -l smallGridNote that
PacmanQAgentis already defined for you in terms of the
QLearningAgentyou've already written.
PacmanQAgentis only different in that it has default learning parameters that are more effective for the Pacman problem (
epsilon=0.05, alpha=0.2, gamma=0.8). You will receive full credit for this question if the command above works without exceptions and your agent wins at least 80% of the last 10 runs.
Hint: If your
QLearningAgent works for
crawler.py but does not seem to be learning a good policy for Pacman on
smallGrid, it may be because your
getPolicy methods do not in some cases properly consider unseen actions. In particular, because unseen actions have by definition a Q-value of zero, if all of the actions that have been seen have negative Q-values, an unseen action may be optimal.
Note: If you want to experiment with learning parameters, you can use the option
-a, for example
-a epsilon=0.1,alpha=0.3,gamma=0.7. These values will then be accessible as
self.epsilon, self.gamma and
self.alpha inside the agent.
Note: While a total of 2010 games will be played, the first 2000 games will not be displayed because of the option
-x 2000, which designates the first 2000 games for training (no output). Thus, you will only see Pacman play the last 10 of these games. The number of training games is also passed to your agent as the option
Note: If you want to watch 10 training games to see what's going on, use the command:
python pacman.py -p PacmanQAgent -n 10 -l smallGrid -a numTraining=10
Make sure you understand what is happening here: the MDP state is the exact board configuration facing Pacman, with the now complex transitions describing an entire ply of change to that state. The intermediate game configurations in which Pacman has moved but the ghosts have not replied are not MDP states, but are bundled in to the transitions.
Once Pacman is done training, he should win very reliably in test games (at least 90% of the time), since now he is exploiting his learned policy.
However, you'll find that training the same agent on the seemingly simple
mediumGrid may not work well. In our implementation, Pacman's average training rewards remain negative throughout training. At test time, he plays badly, probably losing all of his test games. Training will also take a long time, despite its ineffectiveness.
Pacman fails to win on larger layouts because each board configuration is a separate state with separate q-values. He has no way to generalize that running into a ghost is bad for all positions. Obviously, this approach will not scale.
Question 8 (3 points)
Implement an approximate q-learning agent that learns weights for features of states, where many states might share the same features. Write your implementation in
ApproximateQAgent class in
qlearningAgents.py, which is a subclass of
Note: Approximate q-learning assumes the existence of a feature function f(s,a) over state and action pairs, which yields a vector f1(s,a) .. fi(s,a) .. fn(s,a) of feature values. We provide feature functions for you in
featureExtractors.py. Feature vectors are
util.Counter (like a dictionary) objects containing the non-zero pairs of features and values; all omitted features have value zero.
The approximate q-function takes the following form
ApproximateQAgent uses the
IdentityExtractor, which assigns a single feature to every
(state,action) pair. With this feature extractor, your approximate q-learning agent should work identically to
PacmanQAgent. You can test this with the following command:
python pacman.py -p ApproximateQAgent -x 2000 -n 2010 -l smallGrid
ApproximateQAgent is a subclass of
QLearningAgent, and it therefore shares several methods like
getAction. Make sure that your methods in
getQValue instead of accessing q-values directly, so that when you override
getQValue in your approximate agent, the new approximate q-values are used to compute actions.
Once you're confident that your approximate learner works correctly with the identity features, run your approximate q-learning agent with our custom feature extractor, which can learn to win with ease:
python pacman.py -p ApproximateQAgent -a extractor=SimpleExtractor -x 50 -n 60 -l mediumGridEven much larger layouts should be no problem for your
ApproximateQAgent. (warning: this may take a few minutes to train)
python pacman.py -p ApproximateQAgent -a extractor=SimpleExtractor -x 50 -n 60 -l mediumClassic
If you have no errors, your approximate q-learning agent should win almost every time with these simple features, even with only 50 training games.
Grading: We will run your approximate Q-learning agent and check that it learns the same Q-values and feature weights as our reference implementation when each is presented with the same set of examples. To grade your implementation, run the autograder as usual.
Congratulations! You have a learning Pacman agent!