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        "author": "Ond\u0159ej Podsztavek",
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        "description": "OpenAI gym Gridworlds\n=====================\n\nImplementation of three gridworlds environments\nfrom book `Reinforcement Learning: An Introduction\n<http://incompleteideas.net/book/the-book-2nd.html>`_\ncompatible with `OpenAI gym <https://github.com/openai/gym>`_.\n\nUsage\n-----\n\n.. code::\n\n        $ import gym\n        $ import gym_gridworlds\n        $ env = gym.make('Gridworld-v0')  # substitute environment's name\n\n``Gridworld-v0``\n----------------\n\nGridworld is simple 4 times 4 gridworld from example 4.1 in the [book].\nThere are fout action in each state (up, down, right, left)\nwhich deterministically cause the corresponding state transitions\nbut actions that would take an agent of the grid leave a state unchanged.\nThe reward is -1 for all tranistion until the terminal state is reached.\nThe terminal state is in top left and bottom right coners.\n\n``WindyGridworld-v0``\n---------------------\n\nWindy gridworld is from example 6.5 in the book_.\nWindy gridworld is a standard gridworld as described above\nbut there is a crosswind upward through the middle of the grid.\nAction are standard but in the middle region the resultant states are\nshifted upward by a wind which strength varies between columns.\n\n.. _book: http://incompleteideas.net/book/the-book-2nd.html\n\n``Cliff-v0``\n------------\n\nCliff walking is a gridworld example 6.6 from the book_.\nAgain reward is -1 on all transition except those into region\nthat is cliff.\nStepping into this region incurs a reward of -100\nand sends the agent instantly back to the start.\n\n\n",
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