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ogmaneo2's Introduction

OgmaNeo, V2

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Introduction

NOTE: OgmaNeo2 has been replaced by AOgmaNeo

Welcome to the Ogma OgmaNeo2 library, C++ library that contains an implementation of Sparse Predictive Hierarchies.

For an introduction to how the algorithm works, see the presentation. For a more in-depth look, check out the whitepaper.

Installation

CMake

Version 3.13+ of CMake is required when building the library.

OpenMP

This version of OgmaNeo uses OpenMP for multiprocessing. It is required in order to build the library. This will typically already be installed on your system.

Building

The following commands can be used to build the OgmaNeo library:

git clone https://github.com/ogmacorp/OgmaNeo2.git
cd OgmaNeo2 mkdir build
cd build
cmake ..
make
make install

The cmake command can be passed a CMAKE_INSTALL_PREFIX to determine where to install the library and header files.

The BUILD_SHARED_LIBS boolean cmake option can be used to create dynamic/shared object library (default is to create a static library). On Linux it's recommended to add -DBUILD_SHARED_LIBS=ON (especially if you plan to use the Python bindings in PyOgmaNeo2).

make install can be run to install the library. make uninstall can be used to uninstall the library.

On Windows systems it is recommended to use cmake-gui to define which generator to use and specify optional build parameters, such as CMAKE_INSTALL_PREFIX.

Contributions

Refer to the CONTRIBUTING.md file for information on making contributions to OgmaNeo2.

License and Copyright

Creative Commons License
The work in this repository is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. See the OGMANEO_LICENSE.md and LICENSE.md file for further information.

Contact Ogma via [email protected] to discuss commercial use and licensing options.

OgmaNeo Copyright (c) 2016-2020 Ogma Intelligent Systems Corp. All rights reserved.

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ogmaneo2's Issues

Implementation of ESR and 2SI Encoders

I just started looking at the OgmaNeo2 implementation and trying to understand how it works. Within the draft paper, you mention 2 algorithms to encode 2SI and ESR. While going through the code, I only see an implementation of a ImageEncoder. Is that using ESR, 2SI or a different algorithm

New encoder performs significantly worse in RL enviroments

I have tested the new encoder on CartPole-v1 and MiniWorld-Hallway-v0 (from gym_miniworld). The performance is significantly worse, where in CartPole-v1 for example the agent achieves only a slight improvement in 1000 episodes, in comparison to the old encoder, where the agent is able to achieve 500 timestep episodes almost every time in CartPole-v1 after 1000 episodes. The same worsening of performance can be seen in MiniWorld-Hallway-v0. Can you replicate these findings? (For MiniWorld-Hallway-v0 I used layerSizes=6*[Int3(6,6,32)])

I unfortunately can not state the exact reason for this worsening, since I don't fully understand the new encoder. About that, I also have a question not related to this issue: Would it be beneficial to introduce a decorrelation step into the encoders? This should ensure that different columns don't encode the same information which should lead to a higher quality encoding.

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