# Unity ml-agents interface and trainers

The `mlagents` package contains two components : The low level API which allows
you to interact directly with a Unity Environment and a training component whcih
allows you to train agents in Unity Environments using our implementations of
reinforcement learning or imitation learning.

## Installation

The `ml-agents` package can be installed using:

```sh
pip install mlagents
```

or by running the following from the `ml-agents` directory of the repository:

```sh
pip install .
```

## `mlagents.envs`

The ML-Agents toolkit provides a Python API for controlling the agent simulation
loop of a environment or game built with Unity. This API is used by the ML-Agent
training algorithms (run with `mlagents-learn`), but you can also write your
Python programs using this API.

The key objects in the Python API include:

- **UnityEnvironment** — the main interface between the Unity application and
  your code. Use UnityEnvironment to start and control a simulation or training
  session.
- **BrainInfo** — contains all the data from agents in the simulation, such as
  observations and rewards.
- **BrainParameters** — describes the data elements in a BrainInfo object. For
  example, provides the array length of an observation in BrainInfo.

These classes are all defined in the `ml-agents/mlagents/envs` folder of
the ML-Agents SDK.

To communicate with an agent in a Unity environment from a Python program, the
agent must either use an **External** brain or use a brain that is broadcasting
(has its **Broadcast** property set to true). Your code is expected to return
actions for agents with external brains, but can only observe broadcasting
brains (the information you receive for an agent is the same in both cases).

_Notice: Currently communication between Unity and Python takes place over an
open socket without authentication. As such, please make sure that the network
where training takes place is secure. This will be addressed in a future
release._

### Loading a Unity Environment

Python-side communication happens through `UnityEnvironment` which is located in
`ml-agents/mlagents/envs`. To load a Unity environment from a built binary
file, put the file in the same directory as `envs`. For example, if the filename
of your Unity environment is 3DBall.app, in python, run:

```python
from mlagents.env import UnityEnvironment
env = UnityEnvironment(file_name="3DBall", worker_id=0, seed=1)
```

- `file_name` is the name of the environment binary (located in the root
  directory of the python project).
- `worker_id` indicates which port to use for communication with the
  environment. For use in parallel training regimes such as A3C.
- `seed` indicates the seed to use when generating random numbers during the
  training process. In environments which do not involve physics calculations,
  setting the seed enables reproducible experimentation by ensuring that the
  environment and trainers utilize the same random seed.

If you want to directly interact with the Editor, you need to use
`file_name=None`, then press the :arrow_forward: button in the Editor when the
message _"Start training by pressing the Play button in the Unity Editor"_ is
displayed on the screen

### Interacting with a Unity Environment

A BrainInfo object contains the following fields:

- **`visual_observations`** : A list of 4 dimensional numpy arrays. Matrix n of
  the list corresponds to the n<sup>th</sup> observation of the brain.
- **`vector_observations`** : A two dimensional numpy array of dimension `(batch
  size, vector observation size)`.
- **`text_observations`** : A list of string corresponding to the agents text
  observations.
- **`memories`** : A two dimensional numpy array of dimension `(batch size,
  memory size)` which corresponds to the memories sent at the previous step.
- **`rewards`** : A list as long as the number of agents using the brain
  containing the rewards they each obtained at the previous step.
- **`local_done`** : A list as long as the number of agents using the brain
  containing  `done` flags (whether or not the agent is done).
- **`max_reached`** : A list as long as the number of agents using the brain
  containing true if the agents reached their max steps.
- **`agents`** : A list of the unique ids of the agents using the brain.
- **`previous_actions`** : A two dimensional numpy array of dimension `(batch
  size, vector action size)` if the vector action space is continuous and
  `(batch size, number of branches)` if the vector action space is discrete.

Once loaded, you can use your UnityEnvironment object, which referenced by a
variable named `env` in this example, can be used in the following way:  

- **Print : `print(str(env))`**  
  Prints all parameters relevant to the loaded environment and the external
  brains.  
- **Reset : `env.reset(train_model=True, config=None)`**  
  Send a reset signal to the environment, and provides a dictionary mapping
  brain names to BrainInfo objects.  
  - `train_model` indicates whether to run the environment in train (`True`) or
    test (`False`) mode.
  - `config` is an optional dictionary of configuration flags specific to the
    environment. For generic environments, `config` can be ignored. `config` is
    a dictionary of strings to floats where the keys are the names of the
    `resetParameters` and the values are their corresponding float values.
    Define the reset parameters on the Academy Inspector window in the Unity
    Editor.
- **Step : `env.step(action, memory=None, text_action=None)`**  
  Sends a step signal to the environment using the actions. For each brain :
  - `action` can be one dimensional arrays or two dimensional arrays if you have
    multiple agents per brains.
  - `memory` is an optional input that can be used to send a list of floats per
    agents to be retrieved at the next step.
  - `text_action` is an optional input that be used to send a single string per
    agent.

    Returns a dictionary mapping brain names to BrainInfo objects.

    For example, to access the BrainInfo belonging to a brain called
    'brain_name', and the BrainInfo field 'vector_observations':

    ```python
    info = env.step()
    brainInfo = info['brain_name']
    observations = brainInfo.vector_observations
    ```

    Note that if you have more than one external brain in the environment, you
    must provide dictionaries from brain names to arrays for `action`, `memory`
    and `value`. For example: If you have two external brains named `brain1` and
    `brain2` each with one agent taking two continuous actions, then you can
    have:

    ```python
    action = {'brain1':[1.0, 2.0], 'brain2':[3.0,4.0]}
    ```

    Returns a dictionary mapping brain names to BrainInfo objects.  
- **Close : `env.close()`**
  Sends a shutdown signal to the environment and closes the communication
  socket.

## `mlagents.trainers`

1. Open a command or terminal window.
2. Run

```sh
mlagents-learn <trainer-config-path> --run-id=<run-identifier> --train <environment-name>
```

Where:

- `<trainer-config-path>` is the relative or absolute filepath of the trainer
  configuration. The defaults used by environments in the ML-Agents SDK can be
  found in `config/trainer_config.yaml`.
- `<run-identifier>` is a string used to separate the results of different
  training runs
- The `--train` flag tells `mlagents-learn` to run a training session (rather
  than inference)
- `<environment-name>` __(Optional)__ is the path to the Unity executable you
  want to train. __Note:__ If this argument is not passed, the training
  will be made through the editor.

For more detailled documentation, check out the
[ML-Agents toolkit documentation.](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Readme.md)