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README.md
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......@@ -3,12 +3,23 @@
## Overview
Welcome to the repository for the Animal-AI Olympics competition where you will find all the code needed to compete in
this new challenge. Note that for the moment this repo contains **only the training environment** (v0.1) that will be used for the competition and **does not contain any competition tests or information for entering**. If everything goes well the competition will be live on June 30th. Until then we will be continually updating with bug fixes and small changes to environment. However, the general structure will stay the same so it's not too early to start working with the environment. For more information on the competition itself and to stay updated with any developments, head to the [Competition Website](http://www.animalaiolympics.com/) and follow [@MacroPhilosophy](https://twitter.com/MacroPhilosophy) and [@BenBeyret](https://twitter.com/BenBeyret) on twitter.
The environment contains an agent enclosed in a fixed sized arena. Objects can spawn in this arena, including positive and negative rewards (green, yellow and red spheres). All of the hidden tests that will appear in the competition are made using the objects in the training environment. We have provided some sample environment configurations that should be useful for training, but part of the challenge will be experimenting and designing new configurations.
The goal of this first release is to **seek feedback from the community** as well as to provide the environment for research prior to the launch of the competition itself. The competition version of the environment will be similar to this one, however we are open to suggestion (for minor changes) and especially bug reports! Head over to the [issues page](https://github.com/beyretb/AnimalAI-Olympics/issues) and open a ticket using the `suggestion` or `bug` labels
respectively.
this new challenge. Note that for the moment this repo contains **only the training environment** (v0.5) that will be
used for the competition and **does not contain any competition tests or information for entering**. If everything goes
well the competition will be live on July 1st. Until then we will be continually updating with bug fixes and small
changes to the environment. However, the general structure will stay the same so it's not too early to start working with the environment. For more information on the competition itself and to stay updated with any developments, head to the
[Competition Website](http://www.animalaiolympics.com/) and follow [@MacroPhilosophy](https://twitter.com/MacroPhilosophy)
and [@BenBeyret](https://twitter.com/BenBeyret) on twitter.
The environment contains an agent enclosed in a fixed sized arena. Objects can spawn in this arena, including positive
and negative rewards (green, yellow and red spheres). All of the hidden tests that will appear in the competition are
made using the objects in the training environment. We have provided some sample environment configurations that should
be useful for training, but part of the challenge will be experimenting and designing new configurations.
The goal of this first release is to **seek feedback from the community** as well as to provide the environment for
research prior to the launch of the competition itself. The competition version of the environment will be similar to
this one, however we are open to suggestion (for minor changes) and especially bug reports! Head over to the
[issues page](https://github.com/beyretb/AnimalAI-Olympics/issues) and open a ticket using the `suggestion` or `bug`
labels respectively.
To get started install the requirements below, and then follow the [Quick Start Guide](documentation/quickstart.md).
A more in depth documentation <!--, including a primer on animal cognition,--> can be found on the
......@@ -16,37 +27,51 @@ A more in depth documentation <!--, including a primer on animal cognition,--> c
## 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.
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.
1. [Why Animal-AI?](https://mdcrosby.com/blog/animalai1.html)
2. [The Syllabus (Part 1)](https://mdcrosby.com/blog/animalai2.html)
## Requirements
The Animal-AI package works on most platforms. <!--, for cloud engines check out [this cloud documentation](documentation/cloud.md).-->
First of all your will need `python3.6` installed. You will find a list of requirements in the `requirements*.txt` files.
Using `pip` you can run:
3. [The Syllabus (Part 2): Lights Out](https://mdcrosby.com/blog/animalai3.html)
on Linux and mac:
```
pip install -r requirementsOthers.txt
```
## Requirements
on windows:
```
pip install -r requirementsWindows.txt
```
**Note:** `python3.6` is required to install `tensorflow>=1.7,<1.8` which is only used for the training script we provide as an example. Should you wish to use another version of python you can remove the first line from the requirement files. You will still be able to use the `visualizeArena.py` script, but not the `train.py` one.
The Animal-AI package works on Linux, Mac and Windows, as well as most Cloud providers.
<!--, for cloud engines check out [this cloud documentation](documentation/cloud.md).-->
First of all your will need `python3.6` installed. We recommend using a virtual environment specifically for the competition. We provide two packages for
this competition:
- The main one is an API for interfacing with the Unity environment. It contains both a
[gym environment](https://github.com/openai/gym) as well as an extension of Unity's
[ml-agents environments](https://github.com/Unity-Technologies/ml-agents/tree/master/ml-agents-envs). You can install it
via pip:
```
pip install animalai
```
Or you can install it from the source, head to `animalai/` folder and run `pip install -e .`
- We also provide a package that can be used as a starting point for training, and which is required to run most of the
example scripts found in the `examples/` folder. It contains an extension of
[ml-agents' training environment](https://github.com/Unity-Technologies/ml-agents/tree/master/ml-agents) that relies on
[OpenAI's PPO](https://openai.com/blog/openai-baselines-ppo/), as well as
[Google's dopamine](https://github.com/google/dopamine) which implements
[Rainbow](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17204/16680) (among others). You can also install
this package using pip:
```
pip install animalai-train
```
Or you can install it from source, head to `examples/animalai_train` and run `pip install -e .`
Finally download the environment for your system:
| OS | Environment link |
| --- | --- |
| Linux | [download v0.4](https://www.doc.ic.ac.uk/~bb1010/animalAI/env_linux_v0.4.zip) |
| MacOS | [download v0.4](https://www.doc.ic.ac.uk/~bb1010/animalAI/env_mac_v0.4.zip) |
| Windows | [download v0.4](https://www.doc.ic.ac.uk/~bb1010/animalAI/env_windows_v0.4.zip) |
| Linux | [download v0.5](https://www.doc.ic.ac.uk/~bb1010/animalAI/env_linux_v0.5.zip) |
| MacOS | [download v0.5](https://www.doc.ic.ac.uk/~bb1010/animalAI/env_mac_v0.5.zip) |
| Windows | [download v0.5](https://www.doc.ic.ac.uk/~bb1010/animalAI/env_windows_v0.5.zip) |
You can now unzip the content of the archive to the `env` folder and you're ready to go! Make sure the executable
`AnimalAI.*` is in `env/`. On linux you may have to make the file executable by running `chmod +x env/AnimalAI.x86_64`.
......@@ -66,39 +91,43 @@ mode. Here you can control the agent with the following:
| C | switch camera |
| R | reset environment |
**Note**: on some platforms, playing manually in full screen makes the environment slow, keep the environment in window
mode for better performance.
## Competition Tests
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.
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):
For now please cite the [Nature: Machine Intelligence piece](https://rdcu.be/bBCQt) for any work involving the competition environment:
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.
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)
The Python library located in [animalai](animalai) is almost identical to
[ml-agents v0.7](https://github.com/Unity-Technologies/ml-agents/tree/master/ml-agents-envs). We only added the possibility to change the configuration of arenas between episodes. The documentation for ML-Agents can be found [here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Python-API.md).
[ml-agents v0.7](https://github.com/Unity-Technologies/ml-agents/tree/master/ml-agents-envs). We only added the
possibility to change the configuration of arenas between episodes. The documentation for ML-Agents can be found
[here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Python-API.md).
Juliani, A., Berges, V., Vckay, E., Gao, Y., Henry, H., Mattar, M., Lange, D. (2018). [Unity: A General Platform for
Intelligent Agents.](https://arxiv.org/abs/1809.02627) *arXiv preprint arXiv:1809.02627*
## Known Bugs
Occasionally will spawn an empty arena in play mode. Temporary fix: just press R to respawn.
Occasional slow frame rates in play mode. Temporary fix: reduce screen size.
...
## TODO
- [ ] Offer a gym wrapper for training
- [ ] Add protobuf for arena spawning feedback
- [x] Offer a gym wrapper for training
- [x] Improve the way the agent spawns
- [x] Add lights out configurations.
- [x] Improve environment framerates
......@@ -106,17 +135,29 @@ Occasional slow frame rates in play mode. Temporary fix: reduce screen size.
## Version History
- v0.5 Package `animalai`, gym compatible, dopamine example, bug fixes
- Separate environment API and training API in Python
- Release both as `animalai` and `animalai-train` PyPI packages (for `pip` installs)
- Agent speed in play-mode constant across various platforms
- Provide Gym environment
- Add `trainBaselines,py` to train using `dopamine` and the Gym wrapper
- Create the `agent.py` interface for agents submission
- Add the `HotZone` object (equivalent to the red zone but without death)
- v0.4 - Lights off moved to Unity, colors configurations, proportional goals, bugs fixes
- The light is now directly switched on/off within Unity, configuration files stay the same
- Blackouts now work with infinite episodes (`t=0`)
- The `rand_colors` configurations have been removed and the user can now pass `RGB` values, see [here](documentation/configFile.md#objects)
- Rewards for goals are now proportional to their size (except for the `DeathZone`), see [here](documentation/definitionsOfObjects.md#rewards)
- The `rand_colors` configurations have been removed and the user can now pass `RGB` values, see
[here](documentation/configFile.md#objects)
- Rewards for goals are now proportional to their size (except for the `DeathZone`), see
[here](documentation/definitionsOfObjects.md#rewards)
- The agent is now a ball rather than a cube
- Increased safety for spawning the agent to avoid infinite loops
- Bugs fixes
- v0.3 - Lights off, remove Beams and add cylinder
- We added the possibility to switch the lights off at given intervals, see [here](documentation/configFile.md#blackouts)
- We added the possibility to switch the lights off at given intervals, see
[here](documentation/configFile.md#blackouts)
- visualizeLightsOff.py displays an example of lights off, from the agent's point of view
- Beams objects have been removed
- A `Cylinder` object has been added (similar behaviour to the `Woodlog`)
......
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# AnimalAI Python API
This package provides the Python API used for training agents for the Animal AI Olympics competition. It is mostly an
extension of [Unity's MLAgents env](https://github.com/Unity-Technologies/ml-agents/tree/master/ml-agents-envs).
It contains two ways of interfacing with the Unity environments:
- `animalai.envs.environment` contains the `UnityEnvironment` which is similar to the one found in `mlagents` but with
a few adaptations to allow for more custom communications between Python and Unity.
- `animalai.envs.gym.environment` contains the `AnimalAIEnv` which provides a gym environment to use directly with
baselines.
For more details and documentation have a look at the [AnimalAI documentation](../documentation)
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