vsubhashini/RL-bot
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A reinforcement learning agent/bot that navigates an obstacle ridden gridworld. The code is in the src file. To see the options: python gridworld.py -h Usage: gridworld.py [options] Options: -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 WalkGrid, ComboGrid, ObstacleGrid, default WalkGrid) -w X, --windowSize=X Request a window width of X pixels *per grid cell* (default 100) -a A, --agent=A Agent type (options is 'q', default q) -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 Ignore this option. To run Walking agent and view values after 200 iterations: python gridworld.py -a q -k 200 -g WalkGrid -q To run Obstacle agent and view values after 300 iterations: python gridworld.py -a q -k 300 -g ObstacleGrid -q To run Combo agent and view values after 300 iterations: python gridworld.py -a q -k 300 -g ComboGrid -q To run the code in manual mode for 5 iterations: python gridworld.py -a q -k 5 -m -g ObstacleGrid
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A modular reinforcement learning agent/bot that navigates an obstacle ridden dynamic gridworld.
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