{
    "info": {
        "author": "lepisma",
        "author_email": "abhinav.tushar.vs@gmail.com",
        "bugtrack_url": null,
        "classifiers": [],
        "description": "quint\n-----\n\n*No hassle Q-learning library*\n\nQuint is a minimal path finding library useful for discrete state scenarios\n\nUsage\n*****\n\n* Install : ``pip install quint``\n*\tImport : ``from quint import quint``\n*\tInstantiate : ``model = quint(reward_matrix, gamma)`` (refer quint docstring for matrix structure)\n* Override quint.act() to your own action function\n*\tLearn : ``model.learn(final_state, iterations)``\n* Trace : ``model.find_optimum_path(from_state)``\n\n\nMIT License\n\nCopyright (C) 2014 Abhinav Tushar",
        "description_content_type": null,
        "docs_url": null,
        "download_url": "UNKNOWN",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "http://github.com/lepisma/quint",
        "keywords": "q-learning reinforcement learning path finding",
        "license": "MIT",
        "maintainer": null,
        "maintainer_email": null,
        "name": "quint",
        "package_url": "https://pypi.org/project/quint/",
        "platform": "UNKNOWN",
        "project_url": "https://pypi.org/project/quint/",
        "project_urls": {
            "Download": "UNKNOWN",
            "Homepage": "http://github.com/lepisma/quint"
        },
        "release_url": "https://pypi.org/project/quint/0.1/",
        "requires_dist": null,
        "requires_python": null,
        "summary": "Quint is a minimal path finding library useful for discrete state scenarios",
        "version": "0.1"
    },
    "last_serial": 1095066,
    "releases": {
        "0.1": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "4739846d410f69fd879de849f77486e1",
                    "sha256": "c26af20099fdc94ee4ea60e1a105a09389cade9d6a3fe4303518006484b9bd7c"
                },
                "downloads": -1,
                "filename": "quint-0.1.zip",
                "has_sig": false,
                "md5_digest": "4739846d410f69fd879de849f77486e1",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 4789,
                "upload_time": "2014-05-16T21:29:34",
                "url": "https://files.pythonhosted.org/packages/ae/3c/2ccbacc763c1fc4e1628f061e23f95ae83f1a39ec4856164e24fa399b3a6/quint-0.1.zip"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "4739846d410f69fd879de849f77486e1",
                "sha256": "c26af20099fdc94ee4ea60e1a105a09389cade9d6a3fe4303518006484b9bd7c"
            },
            "downloads": -1,
            "filename": "quint-0.1.zip",
            "has_sig": false,
            "md5_digest": "4739846d410f69fd879de849f77486e1",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 4789,
            "upload_time": "2014-05-16T21:29:34",
            "url": "https://files.pythonhosted.org/packages/ae/3c/2ccbacc763c1fc4e1628f061e23f95ae83f1a39ec4856164e24fa399b3a6/quint-0.1.zip"
        }
    ]
}