A topology and parameter evolving universal learning network originally created by Gene Sher.

• asdf
• CMake
• Rebar3

More information on installing Erlang/OTP & Rebar3 can be found here.

apxr_run is a distributed topology and weight evolving artificial neural network originally created by Gene Sher. Significant changes are a deeper integration of the OTP application structure, replacement of the single scape process with a quad tree, converting the sensor -> scape and actuator -> scape processes to be asynchronous, dropping Mnesia in favour of RocksDB and initial work to integrate with Elixir.

$ rebar3 compile

$ rebar3 dialyzer

$ rebar3 do eunit, cover --verbose

$ rebar3 xref

$ rebar3 shell
1> experiment_mgr:run().

$ rebar3 release -d -p priv/lib/elixir/ebin/

$ _build/default/rel/apxr_run/bin/apxr_run console

1> observer:start().

or

1> observer_cli:start().

The experiment_mgr process composes experiments by performing N evolutionary runs, and then produces statistical data and chart ready files of the various evolutionary dynamics and averages.

All these files can be found under the /experiments directory.

Every application of the system needs a corresponding configuration file. The /config directory contains a number of examples that can be used to get started.

Remember, we do not need to force the system to use any one particular approach. We can set it in the constraints to use all available functionality and the system will evolve it all.

Three benchmarks are included:

• Double pole balancing without damping
• Double pole balancing with damping
• Discrete T-maze

See the /doc/examples/benchmarks.md for more information.

Two applications are included:

• Flatland
• FX

See the /doc/examples/applications.md for more information.

The most useful resource for developers working on this project will be the inline documentation.

For a more general introduction, see the /docs directory for a quick introduction to neuroevolution and related topics.

For a more thorough introduction, the book "Handbook of Neuroevolution Through Erlang" by Gene Sher is highly recommended. Indeed, much of the content in the Docs section is taken from this book as is the majority of the inline documentation found in the source code. So, credit for the good stuff goes to him.

Inaka's erlang_guidelines should be used as the basis for things that may be used as a reason to reject a PR.

1. Handbook of Neuroevolution Through Erlang by Gene Sher.
2. Agent-Based Modeling Using Erlang by Gene Sher.

Copyright (C) 2018 - 2019 ApproximateReality - [email protected]