@@ -14,6 +14,14 @@ To get started install the requirements below, and then follow the [Quick Start
A more in depth documentation <!--, including a primer on animal cognition,--> can be found on the
[Documentation Page](documentation/README.md).
## Development Blog
You can read the development blog [here](https://mdcrosby.com/blog). It covers further details about the competition as well as part of the development process.
The Animal-AI package works on most platforms. <!--, for cloud engines check out [this cloud documentation](documentation/cloud.md).-->
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@@ -65,6 +73,12 @@ mode for better performance.
We will be releasing further details about the tests in the competition over the coming weeks. The tests will be split into multiple categories from the very simple (e.g. **food retrieval**, **preferences**, and **basic obstacles**) to the more complex (e.g. **working memory**, **spatial memory**, **object permanence**, and **object manipulation**). For now we have included multiple example config files that each relate to a different category. As we release further details we will also specify the rules for the type of tests that can appear in each category. Note that the example config files are just simple examples to be used as a guide. An agent that solves even all of these perfectly may still not be able to solve all the tests in the categories but it would be off to a very good start.
## Citing
For now please cite the [Nature: Machine Intelligence piece](https://rdcu.be/bBCQt):
Crosby, M., Beyret, B., Halina M. [The Animal-AI Olympics](https://www.nature.com/articles/s42256-019-0050-3) Nature Machine Intelligence 1 (5) p257 2019.
## Unity ML-Agents
The Animal-AI Olympics was built using [Unity's ML-Agents Toolkit.](https://github.com/Unity-Technologies/ml-agents)
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@@ -83,22 +97,21 @@ Occasional slow frame rates in play mode. Temporary fix: reduce screen size.
