Lab 5 Reinforcement Learning in Pacman

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CS 2180– Artificial Intelligence
Lab 5 20 points
Due on 27/4/2022 11.59pm
Instructions: Upload to your moodle account one zip file containing the following. Please do
not submit hardcopy of your solutions. In case moodle is not accessible email the zip file to the
instructor at ckn@iitpkd.ac.in. Late submission is not allowed without prior approval of the
instructor. You are expected to follow the honor code of the course while doing this homework.
1. You can work in teams of size at most 2 for this programming lab. But remember
that every team member should be able to answer questions regarding the
implementation during the viva.
Reinforcement Learning in Pacman
This lab has been adopted from Berkeley Pacman projects.
Introduction
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.
As in previous projects, this project includes an autograder for you to grade your solutions
on your machine. This can be run on all questions with the command:
python autograder.py
It can be run for one particular question, such as q2, by:
python autograder.py -q q2
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It can be run for one particular test by commands of the form:
Files you'll edit:
valueIterationAgents.py A value iteration agent for solving known MDPs.
qlearningAgents.py Q-learning agents for Gridworld, Crawler and Pacman.
analysis.py A file to put your answers to questions given in the project.
Files you should read but NOT edit:
mdp.py Defines methods on general MDPs.
learningAgents.py Defines the base
classes ValueEstimationAgent and QLearningAgent, which your
agents will extend.
util.py Utilities, including util.Counter, which is particularly useful for
Q-learners.
gridworld.py The Gridworld implementation.
featureExtractors.py Classes for extracting features on (state, action) pairs. Used for
the approximate Q-learning agent (in qlearningAgents.py).
deepQLearningAgents.py Training loop for the Deep Q-learning agent.
Files you can ignore:
environment.py Abstract class for general reinforcement learning environments.
Used by gridworld.py.
graphicsGridworldDisplay.pyGridworld graphical display.
graphicsUtils.py Graphics utilities.
textGridworldDisplay.py Plug-in for the Gridworld text interface.
crawler.py The crawler code and test harness. You will run this but not edit
it.
graphicsCrawlerDisplay.py GUI for the crawler robot.
autograder.py Project autograder
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python autograder.py -t test_cases/q2/1-bridge-grid
The code for this project contains the following files
Files to Edit and Submit: You will fill in portions
of valueIterationAgents.py, qlearningAgents.py, and analysis.py during the assignment.
Please do not change the other files in this distribution or submit any of our original files
other than these files.
Note: You only need to submit rl.token, generated by running submission_autograder.py. It
contains the evaluation results from your local autograder, and a copy of all your code. You
do not need to submit any other files.
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.
MDPs
To get started, run Gridworld in manual control mode, which uses the arrow keys:
python gridworld.py -m
testParser.py Parses autograder test and solution files
testClasses.py General autograding test classes
model.py Deep Q Network for helping pacman compute Q values in large
MDPs.
test_cases/ Directory containing the test cases for each question
reinforcementTestClasses.pyProject 6 specific autograding test classes
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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 -q).
As in Pacman, positions are represented by (x,y) Cartesian coordinates and any arrays are
indexed by [x][y], with 'north' being 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 (-r).
Question 1 (5 points): Value Iteration
Recall the value iteration state update equation:
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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 learning 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 kk-step estimates of the optimal values, VkVk. In addition
to runValueIteration, implement the following methods for ValueIterationAgent using VkVk.
• computeActionFromValues(state) computes the best action according to the value
function given by self.values.
• computeQValueFromValues(state, action) returns the Q-value of the (state, action)
pair given by the value function given by self.values.
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 VkVk is computed
from a fixed vector Vk−1Vk−1 (like in lecture), not the “online” version where one single
weight vector is updated in place. This means that when a state’s value is updated in
iteration kk based on the values of its successor states, the successor state values used in
the value update computation should be those from iteration k−1k−1 (even if some of the
successor states had already been updated in iteration kk). The difference is discussed
in Sutton & Barto in Chapter 4.1 on page 91.
Note: A policy synthesized from values of depth kk (which reflect the next kk rewards) will
actually reflect the next k+1k+1 rewards (i.e. you return πk+1πk+1). Similarly, the Q-values
will also reflect one more reward than the values (i.e. you return Qk+1Qk+1).
You should return the synthesized policy πk+1πk+1.
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Hint: You may optionally use the util.Counter class in util.py, which is a dictionary with a
default value of zero. 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).
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), which you can read off of the GUI) and
the empirical resulting average reward (printed after the 10 rounds of execution finish) 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
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Grading: 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).
Question 2 (5 points): Policies
Consider the DiscountGrid layout, shown below. This grid has two terminal states with
positive payoff (in the middle row), a close exit with payoff +1 and a distant exit with payoff
+10. The bottom row of the grid consists of terminal states with negative payoff (shown in
red); each state in this “cliff” region has payoff -10. The starting state is the yellow square.
We distinguish between two types of paths: (1) paths that “risk the cliff” and travel near the
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bottom row of the grid; these paths are shorter but risk earning a large negative payoff, and
are represented by the red arrow in the figure below. (2) paths that “avoid the cliff” and
travel along the top edge of the grid. These paths are longer but are less likely to incur huge
negative payoffs. These paths are represented by the green arrow in the figure below.
In this question, you will choose settings of the discount, noise, and living reward parameters
for this MDP to produce optimal policies of several different types. Your setting of the
parameter values for each part should have the property that, if your agent followed its
optimal policy in the MDP, it would exhibit the given behavior. If a particular behavior is not
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achieved for any setting of the parameters, assert that the policy is impossible by returning
the string 'NOT POSSIBLE'.
Here are the optimal policy types you should attempt to produce:
1. Prefer the close exit (+1), risking the cliff (-10)
2. Prefer the close exit (+1), but avoiding the cliff (-10)
3. Prefer the distant exit (+10), risking the cliff (-10)
4. Prefer the distant exit (+10), avoiding the cliff (-10)
5. Avoid both exits and the cliff (so an episode should never terminate)
To check your answers, run the autograder:
python autograder.py -q q2
question2a() through question2e() should each return a 3-item tuple of (discount, noise,
living reward) in analysis.py.
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.
Grading: We will check that the desired policy is returned in each case.
Question 3 (5 points): Q-Learning
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
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simulated environment like a Gridword, but it’s very important in the real world, where the
real MDP is not available.
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 QLearningAgent in qlearningAgents.py, and you can select it with the option '-a q'. For
this question, you must implement the update, computeValueFromQValues, getQValue,
and computeActionFromQValues methods.
Note: For computeActionFromQValues, 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.
Important: Make sure that in
your computeValueFromQValues and computeActionFromQValues functions, you only access Q
values by calling getQValue . This abstraction will be useful for question 6 when you
override getQValue to use features of state-action pairs rather than state-action pairs
directly.
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 -m
Recall that -k will 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.” Hint: to help with debugging, you can turn off noise by using the --noise
0.0 parameter (though this obviously makes Q-learning less interesting). If you manually
steer Pacman north and then east along the optimal path for four episodes, you should see
the following Q-values:
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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 q3
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Question 4 (3 points): Epsilon Greedy
Complete your Q-learning agent by implementing epsilon-greedy action selection
in getAction, meaning it chooses random actions an epsilon fraction of the time, and follows
its current best Q-values otherwise. Note that choosing a random action may result in
choosing the best action - that is, you should not choose a random sub-optimal action, but
rather any random legal action.
You can choose an element from a list uniformly at random by calling
the random.choice function. You can simulate a binary variable with probability p of success
by using util.flipCoin(p), which returns True with probability p and False with
probability 1-p.
After implementing the getAction method, observe the following behavior of the agent in
gridworld (with epsilon = 0.3).
python gridworld.py -a q -k 100
Your 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 also observe the following simulations for different epsilon values. Does that
behavior of the agent match what you expect?
python gridworld.py -a q -k 100 --noise 0.0 -e 0.1
python gridworld.py -a q -k 100 --noise 0.0 -e 0.9
To test your implementation, run the autograder:
python autograder.py -q q4
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With no additional code, you should now be able to run a Q-learning crawler robot:
python crawler.py
If this doesn’t work, you’ve probably written some code too specific to
the GridWorld problem and you should make it more general to all MDPs.
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 5 (2 points): Q-Learning and Pacman
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.epsilon and 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 smallGrid
Note that PacmanQAgent is already defined for you in terms of the QLearningAgent you’ve
already written. PacmanQAgent is 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 time. The autograder will run 100 test games after the
2000 training games.
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Hint: If your QLearningAgent works for gridworld.py and crawler.py but does not seem to be
learning a good policy for Pacman on smallGrid, it may be because
your getAction and/or computeActionFromQValues 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. Beware of the argmax function from util.Counter!
Note: To grade your answer, run:
python autograder.py -q q5
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 numTraining.
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
During training, you will see output every 100 games with statistics about how Pacman is
faring. Epsilon is positive during training, so Pacman will play poorly even after having
learned a good policy: this is because he occasionally makes a random exploratory move into
a ghost. As a benchmark, it should take between 1000 and 1400 games before Pacman’s
rewards for a 100 episode segment becomes positive, reflecting that he’s started winning
more than losing. By the end of training, it should remain positive and be fairly high
(between 100 and 350).
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
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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 will find that training the same agent on the seemingly simple mediumGrid does
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.
Submission
Submit rl.token, generated by running submission_autograder.py, to Moodle
Note: You only need to submit rl.token, generated by running submission_autograder.py. It
contains the evaluation results from your local autograder, and a copy of all your code. You
do not need to submit any other files.
Reference
[1] http://ai.berkeley.edu/reinforcement.html