Repository containing code implementation for various Anticipatory Learning Classifier Systems (ALCS).
ALCS are is an extension to basic LCS compromising the notation of anticipations. Doing that the systems predominantly are able to anticipate perceptual consequences of actions independent of a reinforcement predictions.
ALCS are able to form complete anticipatory representation (build environment model) which allows faster and more intelligent adaptation of behaviour or problem classification.
Before working with code please install few required dependencies (code is running on Python 3). Make sure Swig binary is installed (required to compile OpenAI Gym environments)
make install_deps
The original author's code is located in assets/original directory.
However it was written in 2001 when C++ was quite different than now. For that reason a slightly changed version (syntax) working on nowadays compilers can be found in assets/ACS2.
To compile the sources type (inside assets/ACS2):
make
And to run it:
./acs2++.out <environment>
For example:
./acs2++.out Envs/Maze4.txt
ACS2 is derived from the original ACS framework. The most important change is that it embodies genetic generalization mechanism. Implementation based on "An Algorithmic Description of ACS2" by Martin V. Butz and Wolfgang Stolzmann.
Prior to PR please execute to check if standards are holding:
make test
make coverage
make pep8