## XCS-RC
*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.
*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).
---
## Relevant links
* [PyPI](https://pypi.org/project/xcs-rc/)
* [Github repo](https://github.com/nuggfr/xcs-rc-python)
* Examples:
* [Classic problems: multiplexer, Markov env](https://github.com/nuggfr/xcs-rc-python)
* [Churn dataset](https://routing.nuggfr.com/churn)
* [Flappy Bird](https://routing.nuggfr.com/flappy)
* [OpenAI Gym](https://routing.nuggfr.com/openai)
---
**Installation**
```
pip install xcs-rc
```
**Initialization**
```
import xcs_rc
agent = xcs_rc.Agent()
```
**Classic Reinforcement Learning cycle**
```
# input: binary string, e.g., "100110" or decimal array
state = str(randint(0, 1))
# pick methods: 0 = explore, 1 = exploit, 2 = explore_it
action = agent.next_action(state, pick_method=1)
# determine reward and apply it, e.g.,
reward = agent.maxreward if action == int(state[0]) else 0.0
agent.apply_reward(reward)
```
**Partially Observable Markov Decision Process (POMDP) environment**
```
# create env and agent
env = xcs_rc.MarkovEnv('maze4') # maze4 is built-in
env.add_agents(num=1, tcomb=100, xmax=50)
agent = env.agents[0]
for episode in range(8000):
steps = env.one_episode(pick_method=2) # returns the number of taken steps
```
**Print population, save it to CSV file, or use append mode**
```
agent.pop.print(title="Population")
agent.save('xcs_population.csv', title="Final XCS Population")
agent.save('xcs_pop_every_100_cycles.csv', title="Cycle: ###", save_mode='a')
```
**Finally, inserting rules to population**
```
# automatically load the last set (important for append mode)
agent.load("xcs_population.csv", empty_first=True)
agent.pop.add(my_list_of_rules) # from a list of classifiers
```
---
## Main Parameters
**XCS-RC Parameters**
* `tcomb`: *combining period*, number of learning cycles before the next rule combining
* `predtol`: *prediction tolerance*, maximum difference between two classifiers to be combined
* `prederrtol`: *prediction error tolerance*, threshold for deletion of inappropriately combined rules
**How to Set**
```
agent.tcomb = 50 # perform rule combining every 50 cycles
agent.predtol = 20.0 # combines rules whose prediction value differences <= 20.0
agent.prederrtol = 10.0 # remove if error > 10.0, after previously below it
```
**Latest updates**
* ~~all related to mutation and crossover is removed~~
* ~~dependencies like pandas and numpy are removed, as well as data science features~~
---
## Results
**Classical Problems: `multiplexer` and `Markov environment`:**
**Flappy Bird from PyGame Learning Environment:**
[](https://youtu.be/Fz05s-stCbE)
**Youtube: CartPole-v0 Benchmark from OpenAI Gym:**
[](https://youtu.be/mJoavWV80MM)
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"description": "## XCS-RC\r\n\r\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.\r\n\r\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).\r\n\r\n---\r\n\r\n## Relevant links\r\n* [PyPI](https://pypi.org/project/xcs-rc/)\r\n* [Github repo](https://github.com/nuggfr/xcs-rc-python)\r\n* Examples:\r\n * [Classic problems: multiplexer, Markov env](https://github.com/nuggfr/xcs-rc-python)\r\n * [Churn dataset](https://routing.nuggfr.com/churn)\r\n * [Flappy Bird](https://routing.nuggfr.com/flappy)\r\n * [OpenAI Gym](https://routing.nuggfr.com/openai)\r\n\r\n---\r\n\r\n**Installation**\r\n```\r\npip install xcs-rc\r\n```\r\n\r\n**Initialization**\r\n```\r\nimport xcs_rc\r\nagent = xcs_rc.Agent()\r\n```\r\n\r\n**Classic Reinforcement Learning cycle**\r\n```\r\n# input: binary string, e.g., \"100110\" or decimal array\r\nstate = str(randint(0, 1))\r\n\r\n# pick methods: 0 = explore, 1 = exploit, 2 = explore_it\r\naction = agent.next_action(state, pick_method=1)\r\n\r\n# determine reward and apply it, e.g.,\r\nreward = agent.maxreward if action == int(state[0]) else 0.0\r\nagent.apply_reward(reward)\r\n```\r\n\r\n**Partially Observable Markov Decision Process (POMDP) environment**\r\n```\r\n# create env and agent\r\nenv = xcs_rc.MarkovEnv('maze4') # maze4 is built-in\r\nenv.add_agents(num=1, tcomb=100, xmax=50)\r\nagent = env.agents[0]\r\n\r\nfor episode in range(8000):\r\n steps = env.one_episode(pick_method=2) # returns the number of taken steps\r\n```\r\n\r\n**Print population, save it to CSV file, or use append mode**\r\n```\r\nagent.pop.print(title=\"Population\")\r\nagent.save('xcs_population.csv', title=\"Final XCS Population\")\r\nagent.save('xcs_pop_every_100_cycles.csv', title=\"Cycle: ###\", save_mode='a')\r\n```\r\n\r\n**Finally, inserting rules to population**\r\n```\r\n# automatically load the last set (important for append mode)\r\nagent.load(\"xcs_population.csv\", empty_first=True)\r\nagent.pop.add(my_list_of_rules) # from a list of classifiers\r\n```\r\n\r\n---\r\n\r\n## Main Parameters\r\n\r\n**XCS-RC Parameters**\r\n* `tcomb`: *combining period*, number of learning cycles before the next rule combining\r\n* `predtol`: *prediction tolerance*, maximum difference between two classifiers to be combined\r\n* `prederrtol`: *prediction error tolerance*, threshold for deletion of inappropriately combined rules\r\n\r\n\r\n**How to Set**\r\n```\r\nagent.tcomb = 50 # perform rule combining every 50 cycles\r\nagent.predtol = 20.0 # combines rules whose prediction value differences <= 20.0\r\nagent.prederrtol = 10.0 # remove if error > 10.0, after previously below it\r\n```\r\n\r\n\r\n**Latest updates**\r\n* ~~all related to mutation and crossover is removed~~\r\n* ~~dependencies like pandas and numpy are removed, as well as data science features~~\r\n\r\n---\r\n\r\n## Results\r\n\r\n**Classical Problems: `multiplexer` and `Markov environment`:**\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n**Flappy Bird from PyGame Learning Environment:**\r\n\r\n\r\n\r\n[](https://youtu.be/Fz05s-stCbE)\r\n\r\n**Youtube: CartPole-v0 Benchmark from OpenAI Gym:**\r\n\r\n[](https://youtu.be/mJoavWV80MM)\r\n",
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