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Quick Commands
python autograder.py -q q2
python autograder.py -t test_cases/q2/1-bridge-grid

Edit Files
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.


Getting Familiar
python gridworld.py -m
python gridworld.py -h
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  -h, --help            show this help message and exit
  -d DISCOUNT, --discount=DISCOUNT
                        Discount on future (default 0.9)
  -r R, --livingReward=R
                        Reward for living for a time step (default 0.0)
  -n P, --noise=P       How often action results in unintended direction
                        (default 0.2)
  -e E, --epsilon=E     Chance of taking a random action in q-learning
                        (default 0.3)
  -l P, --learningRate=P
                        TD learning rate (default 0.5)
  -i K, --iterations=K  Number of rounds of value iteration (default 10)
  -k K, --episodes=K    Number of epsiodes of the MDP to run (default 1)
  -g G, --grid=G        Grid to use (case sensitive; options are BookGrid,
                        BridgeGrid, CliffGrid, MazeGrid, default BookGrid)
  -w X, --windowSize=X  Request a window width of X pixels *per grid cell*
                        (default 150)
  -a A, --agent=A       Agent type (options are 'random', 'value' and 'q',
                        default random)
  -t, --text            Use text-only ASCII display
  -p, --pause           Pause GUI after each time step when running the MDP
  -q, --quiet           Skip display of any learning episodes
  -s S, --speed=S       Speed of animation, S > 1.0 is faster, 0.0 < S < 1.0
                        is slower (default 1.0)
  -m, --manual          Manually control agent
  -v, --valueSteps      Display each step of value iteration


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Reference Files
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).

Q4 Notes
update, computeValueFromQValues, getQValue, and computeActionFromQValues methods.
python autograder.py -q q4