JordanMSchall/Project3
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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 -------------------------------------------------------- -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 -------------------------------------------------------- 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
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