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    "info": {
        "author": "Nugroho Fredivianus",
        "author_email": "nuggfr@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "## XCS-RC\n\n*Accuracy-based Learning Classifier Systems* with **Rule Combining** mechanism, shortly `XCS-RC` for Python3, loosely based on Martin Butz's XCS Java code (2001). Read my PhD thesis [here](https://publikationen.bibliothek.kit.edu/1000046880) for the complete algorithmic description.\n\n*Rule Combining* is novel function that employs inductive reasoning, replacing ~~all Darwinian genetic operation like mutation and crossover~~. It can handle `binaries` and `real`, reaching better *correctness rate* and *population size* quicker than several XCS instances. My earlier papers comparing them can be obtained at [here](https://link.springer.com/chapter/10.1007/978-3-642-17298-4_30) and [here](https://dl.acm.org/citation.cfm?id=2331009).\n\n---\n\n## Relevant links\n* [PyPI](https://pypi.org/project/xcs-rc/)\n* [Github repo](https://github.com/nuggfr/xcs-rc-python)\n* Examples:\n    * [Classic problems: multiplexer, Markov env](https://github.com/nuggfr/xcs-rc-python)\n    * [Churn dataset](https://routing.nuggfr.com/churn)\n    * [Flappy Bird](https://routing.nuggfr.com/flappy)\n    * [OpenAI Gym](https://routing.nuggfr.com/openai)\n\n---\n\n**Installation**\n```\npip install xcs-rc\n```\n\n**Initialization**\n```\nimport xcs_rc\nagent = xcs_rc.Agent()\n```\n\n**Classic Reinforcement Learning cycle**\n```\n# input: binary string, e.g., \"100110\" or decimal array\nstate = str(randint(0, 1))\n\n# pick methods: 0 = explore, 1 = exploit, 2 = explore_it\naction = agent.next_action(state, pick_method=1)\n\n# determine reward and apply it, e.g.,\nreward = agent.maxreward if action == int(state[0]) else 0.0\nagent.apply_reward(reward)\n```\n\n**Partially Observable Markov Decision Process (POMDP) environment**\n```\n# create env and agent\nenv = xcs_rc.MarkovEnv('maze4')  # maze4 is built-in\nenv.add_agents(num=1, tcomb=100, xmax=50)\nagent = env.agents[0]\n\nfor episode in range(8000):\n    steps = env.one_episode(pick_method=2)  # returns the number of taken steps\n```\n\n**Data classification**\n```\nagent.train(X_train, y_train)\ncm = agent.test(X_test, y_test)  # returns the confusion matrix\npreds, probs = agent.predict(X)  # returns lists of predictions and probabilities\n```\n\n**Print population, save it to CSV file, or use append mode**\n```\nagent.pop.print(title=\"Population\")\nagent.save('xcs_population.csv', title=\"Final XCS Population\")\nagent.save('xcs_pop_every_100_cycles.csv', title=\"Cycle: ###\", save_mode='a')\n```\n\n**Finally, inserting rules to population**\n```\n# automatically load the last set (important for append mode)\nagent.load(\"xcs_population.csv\", empty_first=True)\nagent.pop.add(my_list_of_rules)  # from a list of classifiers\n```\n\n---\n\n## Main Parameters\n\n**XCS-RC Parameters**\n* `tcomb`: *combining period*, number of learning cycles before the next rule combining\n* `predtol`: *prediction tolerance*, maximum difference between two classifiers to be combined\n* `prederrtol`: *prediction error tolerance*, threshold for deletion of inappropriately combined rules\n\n\n**How to Set**\n```\nagent.tcomb = 50 # perform rule combining every 50 cycles\nagent.predtol = 20.0 # combines rules whose prediction value differences <= 20.0\nagent.prederrtol = 10.0 # remove if error > 10.0, after previously below it\n```\n\n\n**Parameters from original XCS**\n* ~~all related to mutation and crossover is removed~~\n* the others are kept and accessible (e.g., `agent.alpha = 0.15`)\n\n---\n\n## Results\n\n**Classical Problems: `multiplexer` and `Markov environment`:**\n\n![Binary MP11-HIGH](https://raw.githubusercontent.com/nuggfr/xcs-rc-python/master/xcs-rc-mp11-binary.png)\n\n![Real MP6-HIGH](https://raw.githubusercontent.com/nuggfr/xcs-rc-python/master/xcs-rc-mp6-real.png)\n\n![Markov Maze4](https://raw.githubusercontent.com/nuggfr/xcs-rc-python/master/xcs-rc-markov-maze4.png)\n\n**Flappy Bird from PyGame Learning Environment:**\n\n![Flappy Bird XCS-RC plot](https://raw.githubusercontent.com/nuggfr/xcs-rc-python/master/flappy_plot.png)\n\n[![Flappy Bird XCS-RC youtube](https://img.youtube.com/vi/Fz05s-stCbE/0.jpg)](https://youtu.be/Fz05s-stCbE)\n\n**Youtube: CartPole-v0 Benchmark from OpenAI Gym:**\n\n[![CartPole XCS-RC](https://img.youtube.com/vi/mJoavWV80MM/0.jpg)](https://youtu.be/mJoavWV80MM)\n\n\n",
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