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<img height="300" src="documentation/PrefabsPictures/steampunkFOURcrop.png">
</p>
| ![](examples/notebook_data/animal-cyl-fail.gif) | ![](examples/notebook_data/agent-cyl-fail.gif) |
|---|---|
| ![](examples/notebook_data/animal-cyl-pass.gif) | ![](examples/notebook_data/agent-cyl-pass.gif) |
## Overview
The [Animal-AI](http://animalaiolympics.com/AAI) is a project which introduces the study of animal cognition to the world of AI.
......
%% Cell type:markdown id: tags:
# Animal-AI Environment tutorial
This tutorial is a step-by-step presentation of the new version of the Animal-AI library. The new Animal-AI environment is quite similar to the version used for the competition, however the `animalai` and `animalai_train` APIs have been dramatically improved, reflecting the great improvements made by [Unity ml-agents](https://github.com/Unity-Technologies/ml-agents).
In this notebook, **we present the environment and how to design both your training and testing setups**. In the [second notebook (training)](./training_tutorial.pynb) we'll show you how to train an agent to solve a task it has never seen before.
In this notebook, **we present the environment and how to design both your training and testing setups**. In the second notebook (training) we'll show you how to train an agent to solve a task it has never seen before.
%% Cell type:markdown id: tags:
## Introducing animal cognition to the AI world
Our goal is to provide a tool for researchers to go beyond classical RL environments, allowing you to develop agents that possess cogintive skills similar to animal's. The main idea is to be able to test and/or train your agents on **experiments taken or inspired from real life animal experiments**. This repository holds 900 such experiments which cover a dozen cognitive skills. You can find more details on the test-bed on [our website](http://animalaiolympics.com/AAI/testbed)
The environment is a simple arena with an agent that can only move left, right, forward and backward, aiming to collect positive reward and avoid negative ones. It can also hold [several objects](../documentation/definitionsOfObjects.md) which can be used to set up experiments. You can really put yourself in the shoes of an animal cognition scientist building experiments with whatever you can find in a lab.
The environment is a simple arena with an agent that can only move left, right, forward and backward, aiming to collect positive reward and avoid negative ones. It can also hold [several objects](https://github.com/beyretb/AnimalAI-Olympics/blob/master/documentation/definitionsOfObjects.md) which can be used to set up experiments. You can really put yourself in the shoes of an animal cognition scientist building experiments with whatever you can find in a lab.
From the agent's perspective, a classical experiment called a Y-maze looks like this(the agent must explore a simple Y-shaped maze to find a reward, often food):
<img src="documentation/notebook_data/y_maze.png" width="40%">
<img src="notebook_data/y_maze.png" width="40%">
The agent is on an elevated platform (blue), needs to move towards the reward (green ball) and avoid going to the right in which case the agent would be stuck (the platform is too high for the agent to climb back on).
From an RL perspective this might seem like a trivial problem to solve! In a classical RL setup where you train and test on the same problem it is indeed simple. However, when tested on a similar task, an animal would encounter this problem for the first time. And this is what we encourage you to do as well: **create your own training curriculum, and use our experiments as a test set your agent has never seen before**. We believe this is needed to truly test an agent's capacity to acquire cognitive skills.
But enough chit-chat, let's dive right in with an example!
%% Cell type:code id: tags:
``` python
from IPython.display import HTML
```
%% Cell type:markdown id: tags:
## Can your agent self control? - Part I
Self control is hard, we've all been there (looking at you chocolate bar). But don't worry, this is something a lot of species struggle with. In [The evolution of self-control](https://www.pnas.org/content/111/20/E2140) MacLean et al. tested this ability in **36 different species**! In a very simple experiment, animals are offered food they can reach easily by reaching out to it. But then, they're shown the same food behind a transparent wall, they need to go around the wall to grab the food. They can see the food just as well, but they need to refrain from reaching out like before.
Below are videos of such animals, as well as two participants' submissions to our compeition, exhibiting similar behaviors (remember, these agents never encoutered this task during training):
%% Cell type:code id: tags:
``` python
HTML('<div><div><video width="24%" playsinline="" autoplay="" muted="" loop=""><source src="notebook_data/animal-cyl-pass.mp4" type="video/mp4"></video><video width="24%" " playsinline="" autoplay="" muted="" loop=""><source src="notebook_data/agent-cyl-pass.mp4" type="video/mp4"></video><video width="24%" playsinline="" autoplay="" muted="" loop=""><source src="notebook_data/animal-cyl-fail.mp4" type="video/mp4"></video><video width="24%" playsinline="" autoplay="" muted="" loop=""><source src="notebook_data/agent-cyl-fail.mp4" type="video/mp4"></video></div>')
```
%%%% Output: execute_result
<IPython.core.display.HTML object>
%% Cell type:markdown id: tags:
In the following sections we'll design a training curriculum which does not include the exact "reward in a transparent cylinder" task, but which we can use to train an agent that can solve this same task. In the [next tutorial](./training_tutorial.pynb), we'll train such an agent using this curriculum.
In the following sections we'll design a training curriculum which does not include the exact "reward in a transparent cylinder" task, but which we can use to train an agent that can solve this same task. In the training tutorial, we'll train such an agent using this curriculum.
%% Cell type:markdown id: tags:
## Let's get started: experiments design
First things first, as rigorous researchers, we want to design a good training environment. To do so, we provide a [list of items](../documentation/definitionsOfObjects.md) you can include in your arena, you can have a look at the details later, this section highlights the basics.
First things first, as rigorous researchers, we want to design a good training environment. To do so, we provide a [list of items](https://github.com/beyretb/AnimalAI-Olympics/blob/master/examples/environment_tutorial.ipynb) you can include in your arena, you can have a look at the details later, this section highlights the basics.
To begin with let's train an agent to collect food right in front of it, as simple as that! To do so, you'll need to design a `yaml` file which describes the experiment setup. It contains:
- experiment parameters (maximum steps, steps at which the light is turned on/off)
- a list of objects
- their specifications (positions, rotations, sizes, colors) which are randomized if not provided
Below is the simplest example possible:
%% Cell type:code id: tags:
``` python
with open('configurations/curriculum/0.yml') as f:
print(f.read())
```
%%%% Output: stream
!ArenaConfig
arenas:
-1: !Arena
pass_mark: 0
t: 250
items:
- !Item
name: Agent
positions:
- !Vector3 {x: 20, y: 0, z: 20}
rotations: [0]
- !Item
name: GoodGoal
positions:
- !Vector3 {x: 20, y: 0, z: 22}
sizes:
- !Vector3 {x: 1, y: 1, z: 1}
%% Cell type:markdown id: tags:
This file contains the configuration of one arena (`!Arena`), with only the agent on the ground (`y=0`) in the center (`x=20`,`z=20`) and a `GoodGoal` (green sphere) of size 1 in front of it (`x=20`,`z=22`). Pretty simple right!
One _little trick_ we used here: one environment can contain several arenas during training, each with its own configuration. This allows your training algorithm to collect more observations at once. You can just place the configurations one after the others like this:
```
!ArenaConfig
arenas:
0: !Arena
......
1: !Arena
......
2: !Arena
......
```
But if you want all the arenas in the environment to have the same configuration then do as we did above: define one configuration only with key `-1`
You can now use this to load an environment and play yourself ([this script does that for you](./load_config_and_play.py)). Make sure you have followed the [installation guide](../README.md#requirements) and then create an `AnimalAIEnvironment` in play mode:
You can now use this to load an environment and play yourself (`load_config_and_play.py` does that for you). Make sure you have followed the [installation guide](https://github.com/beyretb/AnimalAI-Olympics#requirements) and then create an `AnimalAIEnvironment` in play mode:
%% Cell type:code id: tags:
``` python
from animalai.envs.arena_config import ArenaConfig
from animalai.envs.environment import AnimalAIEnvironment
from mlagents_envs.exception import UnityCommunicationException
try:
environment = AnimalAIEnvironment(
file_name='env/AnimalAI',
base_port=5005,
arenas_configurations=ArenaConfig('configurations/curriculum/0.yml'),
play=True,
)
except UnityCommunicationException:
# you'll end up here if you close the environment window directly
# always try to close it from script
environment.close()
```
%% Cell type:markdown id: tags:
Press **C** to change the viewpoint (bird's eye, first person, third person), and move with **W,A,S,D** or the **arrows** on your keyboard. Once you're done, let's close this environment:
%% Cell type:code id: tags:
``` python
if environment:
environment.close() # takes a few seconds
```
%% Cell type:markdown id: tags:
## Creating a curriculum
Such a training set is not going to get us very far... The food is right before the agent, it won't even learn any sort of exploration, not even turning around to see if the food is behing it. [Curriculum learning](https://lilianweng.github.io/lil-log/2020/01/29/curriculum-for-reinforcement-learning.html) uses a set of training configurations of increasing difficulty in order to learn a complex task. Think "stand up before you walk, walk before you run" type of learning.
To solve our problem, we might want to have the following consecutive learning steps:
1. food right in from of the agent (example above)
2. food in front of us further away
3. food at the same distance as 2, but randomize the agent's rotation (might be behind the agent)
4. agent and food randomly placed and rotated each on a fixed z-axis, and small transparent wall in between the two
5. same as 4 with bigger and bigger walls
To design a curriculum, we need to place all the yaml files in a folder along with a json configuration file which contains the details of when to switch from one level to the next. The above curriculum can be found in [this folder](./examples/configurations/curriculum).
To design a curriculum, we need to place all the yaml files in a folder along with a json configuration file which contains the details of when to switch from one level to the next. The above curriculum can be found in `configurations/curriculum`.
The second configuration is just like the first but with say `z: 35` for `GoodGoal`. The third one only requires to randomize the rotation, to do so we can replace the `rotations: [0]` with `rotations: [-1]` as any parameter with a value of `-1` is randomized. Otherwise you can just remove the `rotations` line altogether, and the rotation will be randomized automatically (also works with positions, sizes and colors).
Putting all of the above together, we can have a look at step 4:
%% Cell type:code id: tags:
``` python
configuration = 'configurations/curriculum/3.yml'
with open(configuration) as f:
print(f.read())
try:
environment = AnimalAIEnvironment(
file_name='env/AnimalAI',
base_port=5005,
arenas_configurations=ArenaConfig(configuration),
play=True,
)
except UnityCommunicationException:
# you'll end up here if you close the environment window directly
# always try to close it from script
environment.close()
```
%%%% Output: stream
!ArenaConfig
arenas:
0: !Arena
pass_mark: 0
t: 250
items:
- !Item
name: Agent
positions:
- !Vector3 {x: -1, y: 0, z: 5}
- !Item
name: GoodGoal
positions:
- !Vector3 {x: -1, y: 0, z: 35}
sizes:
- !Vector3 {x: 1, y: 1, z: 1}
- !Item
name: WallTransparent
positions:
- !Vector3 {x: -1, y: 0, z: 20}
sizes:
- !Vector3 {x: 10, y: 5, z: 1}
rotations: [0]
%% Cell type:markdown id: tags:
Play a few runs with this configuration, you'll see the various items randomly appearing along a given axis. You can **press R** to reset the environment. Once you're done, close the environment:
%% Cell type:code id: tags:
``` python
if environment:
environment.close() # takes a few seconds
```
%% Cell type:markdown id: tags:
Finally, the file `AnimalAI.json` holds the parameters for the process (the name of this file **must remain AnimalAI.json**), which looks like this:
%% Cell type:code id: tags:
``` python
with open('configurations/curriculum/AnimalAI.json') as f:
print(f.read())
```
%%%% Output: stream
{
"measure": "reward",
"thresholds": [
0.8,
0.8,
0.8,
0.8,
0.8
],
"min_lesson_length": 100,
"signal_smoothing": true,
"configuration_files": [
"0.yml",
"1.yml",
"2.yml",
"3.yml",
"4.yml",
"5.yml"
]
}
%% Cell type:markdown id: tags:
This tells you that we'll switch from one level to the next once the reward per episod is above 0.8. Easy right?
In the next notebook we'll use the above curriculum example to train an agent that can solve the tube task we saw in the videos earlier. Before that, it is worth looking at an extra feature of the environment (blackouts) and use that to interact with the environment from python rather than playing manually.
%% Cell type:markdown id: tags:
## Interacting with the environment + bonus light switch!
In this final part, we look at the API for interacting with the environment. Namely, we want to take steps, collect observations and rewards. For this part we'll load an environment which tests for a cognitive skill called **objects permanence**. It tests the capacity of an agent to understand that an object still exist even if it moved out of sight, think of a car turning a corner, we all know the car hasn't vanished from existence. This test introduces another feature of the environment: **the light switch** which allows to switch the light in the environment on and off. Let's have a look at the experiment:
%% Cell type:code id: tags:
``` python
light_switch_conf = 'configurations/arena_configurations/light_switch.yml'
try:
environment = AnimalAIEnvironment(
file_name='env/AnimalAI',
base_port=5005,
arenas_configurations=ArenaConfig(light_switch_conf),
play=True,
)
except UnityCommunicationException:
# you'll end up here if you close the environment window directly
# always try to close it from script
environment.close()
```
%% Cell type:markdown id: tags:
Change the point of view by pressing **C** and then reset by pressing **R**. As you can see the light switches off just before you can see the reward disappear, but I guess you figured where it had gone right?
To do this you just need to add the `blackouts` parameter to your configuration file. It's a list of frames at wich the light switches on and off and on and off and so on. Below we switch off at frame 20 and back on at 20. You can **also provide a negative number** such as `blackouts: [-20]` to switch on/off every 20 frames.
%% Cell type:code id: tags:
``` python
if environment:
environment.close() # takes a few seconds
with open(light_switch_conf) as f:
print(f.read())
```
%%%% Output: stream
!ArenaConfig
arenas:
0: !Arena
pass_mark: 0
t: 500
blackouts: [20,50]
items:
- !Item
name: GoodGoalBounce
positions:
- !Vector3 {x: 30, y: 0, z: 30}
rotations: [270]
sizes:
- !Vector3 {x: 1, y: 1, z: 1}
- !Item
name: Wall
positions:
- !Vector3 {x: 7.5, y: 0, z: 25}
rotations: [90]
sizes:
- !Vector3 {x: 1, y: 3, z: 15}
colors:
- !RGB {r: 153, g: 153, b: 153}
- !Item
name: Ramp
positions:
- !Vector3 {x: 10, y: 0, z: 30}
rotations: [90]
sizes:
- !Vector3 {x: 1, y: 0.2, z: 1}
colors:
- !RGB {r: 153, g: 153, b: 153}
- !Item
name: Agent
positions:
- !Vector3 {x: 20, y: 0, z: 5}
rotations: [0]
%% Cell type:markdown id: tags:
To finish this tutorial on the environment and associated API, we look at how we can interact from Python. To do so we'll launch the environment without the play mode, this will allow a communicator between Python and Unity to exchange actions and observations. We will also set the camera resolution for our agent's observations:
%% Cell type:code id: tags:
``` python
resolution=256
try:
environment = AnimalAIEnvironment(
file_name='env/AnimalAI',
base_port=5006,
resolution=resolution,
)
except UnityCommunicationException:
# you'll end up here if you close the environment window directly
# always try to close it from script
environment.close()
```
%% Cell type:markdown id: tags:
We can first retrieve the various caracteristics of the environment:
%% Cell type:code id: tags:
``` python
agent_groups = environment.get_agent_groups()
agent_group_spec = environment.get_agent_group_spec(agent_groups[0])
print(f'Here we only have {len(agent_groups)+1} agent group: {agent_groups[0]}')
print(f'''\nAnd you can get their caracteristics: \n
visual observations shape: {agent_group_spec.observation_shapes[0]}
vector observations shape (velocity): {agent_group_spec.observation_shapes[1]}
actions are discrete: {agent_group_spec.action_type}
actions have shape: {agent_group_spec.action_shape}
''')
```
%%%% Output: stream
Here we only have 2 agent group: AnimalAI?team=0
And you can get their caracteristics:
visual observations shape: (256, 256, 3)
vector observations shape (velocity): (3,)
actions are discrete: ActionType.DISCRETE
actions have shape: (3, 3)
%% Cell type:markdown id: tags:
As you can notice we did not pass an arena configuration file. You can actually pass one to the environment at any point during training when you reset the environment. Let's do that now:
%% Cell type:code id: tags:
``` python
environment.reset(arenas_configurations=ArenaConfig(light_switch_conf))
```
%% Cell type:markdown id: tags:
Finally, we can now take actions and collect observations and rewards! This is done in two steps as opposed to the classical Gym setup Unity allows agent to request actions when they need only, but that's not very relevent for us.
%% Cell type:code id: tags:
``` python
import numpy as np
actions = [[0,0]]*50 # Do nothing until the lights come back on
actions += [[1,0]]*40 # Go forward
actions += [[0,2]]*15 # turn left
actions += [[1,0]]*50 # go forward again
agent_group = agent_groups[0]
visual_observations = []
velocity_observations = []
rewards = []
done = False
step = 0
while not done and step<len(actions):
action = np.array(actions[step]).reshape(1,2)
environment.set_actions(agent_group=agent_group, action=action)
environment.step()
step_result = environment.get_step_result(agent_group)
visual_observations.append(step_result.obs[0])
velocity_observations.append(step_result.obs[1])
done = step_result.done[0]
rewards.append(step_result.reward[0])
max_step_reached = step_result.max_step[0]
step+=1
# environment.close()
```
%% Cell type:markdown id: tags:
Don't worry if you didn't see anything happening in the Unity window. To conclude let's look at what we retrieved:
%% Cell type:code id: tags:
``` python
from matplotlib import pyplot as plt
from matplotlib import animation
from IPython.display import display, clear_output
fig, ax = plt.subplots()
vals = ax.imshow(np.zeros((resolution, resolution,3)))
cumulative_reward = 0
# visual_observations[0].shape
for i in range(len(visual_observations)):
vals.set_data(visual_observations[i].reshape((resolution, resolution,3)))
clear_output(wait=True)
display(fig)
cumulative_reward+=rewards[i]
print(f'Step {i}')
print(f'Forward velocity \t {velocity_observations[i][0][2]:0.2f}')
print(f'Right velocity \t\t {velocity_observations[i][0][0]:0.2f}')
print(f'Up velocity \t\t {velocity_observations[i][0][1]:0.2f}')
print(f'Cumulative reward {cumulative_reward:0.2f}')
```
%%%% Output: display_data
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)
%%%% Output: stream
Step 144
Forward velocity 8.37
Right velocity 0.01
Up velocity 0.00
Cumulative reward 0.71
%%%% Output: display_data
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)
%% Cell type:code id: tags:
``` python
if environment:
environment.close() # takes a few seconds
```
%% Cell type:markdown id: tags:
Taht's pretty much it! In practice we provide a very efficient environments manager (derived from ml-agents). It allows the training loop to run over several instances of the environment for better performances. You can also use the Gym implementation of the environment. See the examples on training with OpenAI baselines for that.
If you want to train using ml-agents, it is a very modular framework which requires some work to plug your own meodel in (depending on how far they are from PPO or SAC), but it's worth it!
Have a look at the second notebook on [training an agent using animalai-train](training_tutorial.ipynb)
Have a look at the second notebook on training an agent using animalai-train.
%% Cell type:code id: tags:
``` python
```
......