ml-agents/docs/Python-API.md

Unity ML-Agents Python Low Level API

The mlagents Python package contains two components: a low level API which allows you to interact directly with a Unity Environment (mlagents_envs) and an entry point to train (mlagents-learn) which allows you to train agents in Unity Environments using our implementations of reinforcement learning or imitation learning. This document describes how to use the mlagents_envs API. For information on using mlagents-learn, see here.

The Python Low Level API can be used to interact directly with your Unity learning environment. As such, it can serve as the basis for developing and evaluating new learning algorithms.

mlagents_envs

The ML-Agents Toolkit Low Level API is a Python API for controlling the simulation loop of an environment or game built with Unity. This API is used by the training algorithms inside the ML-Agent Toolkit, but you can also write your own 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.
• BehaviorName - is a string that identifies a behavior in the simulation.
• AgentId - is an int that serves as unique identifier for Agents in the simulation.
• DecisionSteps — contains the data from Agents belonging to the same "Behavior" in the simulation, such as observations and rewards. Only Agents that requested a decision since the last call to env.step() are in the DecisionSteps object.
• TerminalSteps — contains the data from Agents belonging to the same "Behavior" in the simulation, such as observations and rewards. Only Agents whose episode ended since the last call to env.step() are in the TerminalSteps object.
• BehaviorSpec — describes the shape of the observation data inside DecisionSteps and TerminalSteps as well as the expected action shapes.

These classes are all defined in the base_env script.

An Agent "Behavior" is a group of Agents identified by a BehaviorName that share the same observations and action types (described in their BehaviorSpec). You can think about Agent Behavior as a group of agents that will share the same policy. All Agents with the same behavior have the same goal and reward signals.

To communicate with an Agent in a Unity environment from a Python program, the Agent in the simulation must have Behavior Parameters set to communicate. You must set the Behavior Type to Default and give it a Behavior Name.

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 environment.py. 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, in python, run:

from mlagents_envs.environment import UnityEnvironment
# This is a non-blocking call that only loads the environment.
env = UnityEnvironment(file_name="3DBall", seed=1, side_channels=[])
# Start interacting with the environment.
env.reset()
behavior_names = env.behavior_specs.keys()
...

NOTE: Please read Interacting with a Unity Environment to read more about how you can interact with the Unity environment from Python.

• 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 are deterministic, setting the seed enables reproducible experimentation by ensuring that the environment and trainers utilize the same random seed.
• side_channels provides a way to exchange data with the Unity simulation that is not related to the reinforcement learning loop. For example: configurations or properties. More on them in the Modifying the environment from Python section.

If you want to directly interact with the Editor, you need to use file_name=None, then press the Play 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

The BaseEnv interface

A BaseEnv has the following methods:

• Reset : env.reset() Sends a signal to reset the environment. Returns None.
• Step : env.step() Sends a signal to step the environment. Returns None. Note that a "step" for Python does not correspond to either Unity Update nor FixedUpdate. When step() or reset() is called, the Unity simulation will move forward until an Agent in the simulation needs a input from Python to act.
• Close : env.close() Sends a shutdown signal to the environment and terminates the communication.
• Behavior Specs : env.behavior_specs Returns a Mapping of BehaviorName to BehaviorSpec objects (read only). A BehaviorSpec contains the observation shapes and the ActionSpec (which defines the action shape). Note that the BehaviorSpec for a specific group is fixed throughout the simulation. The number of entries in the Mapping can change over time in the simulation if new Agent behaviors are created in the simulation.
• Get Steps : env.get_steps(behavior_name: str) Returns a tuple DecisionSteps, TerminalSteps corresponding to the behavior_name given as input. The DecisionSteps contains information about the state of the agents that need an action this step and have the behavior behavior_name. The TerminalSteps contains information about the state of the agents whose episode ended and have the behavior behavior_name. Both DecisionSteps and TerminalSteps contain information such as the observations, the rewards and the agent identifiers. DecisionSteps also contains action masks for the next action while TerminalSteps contains the reason for termination (did the Agent reach its maximum step and was interrupted). The data is in np.array of which the first dimension is always the number of agents note that the number of agents is not guaranteed to remain constant during the simulation and it is not unusual to have either DecisionSteps or TerminalSteps contain no Agents at all.
• Set Actions :env.set_actions(behavior_name: str, action: ActionTuple) Sets the actions for a whole agent group. action is an ActionTuple, which is made up of a 2D np.array of dtype=np.int32 for discrete actions, and dtype=np.float32 for continuous actions. The first dimension of np.array in the tuple is the number of agents that requested a decision since the last call to env.step(). The second dimension is the number of discrete or continuous actions for the corresponding array.
• Set Action for Agent : env.set_action_for_agent(agent_group: str, agent_id: int, action: ActionTuple) Sets the action for a specific Agent in an agent group. agent_group is the name of the group the Agent belongs to and agent_id is the integer identifier of the Agent. action is an ActionTuple as described above. Note: If no action is provided for an agent group between two calls to env.step() then the default action will be all zeros.

DecisionSteps and DecisionStep

DecisionSteps (with s) contains information about a whole batch of Agents while DecisionStep (no s) only contains information about a single Agent.

A DecisionSteps has the following fields :

• obs is a list of numpy arrays observations collected by the group of agent. The first dimension of the array corresponds to the batch size of the group (number of agents requesting a decision since the last call to env.step()).
• reward is a float vector of length batch size. Corresponds to the rewards collected by each agent since the last simulation step.
• agent_id is an int vector of length batch size containing unique identifier for the corresponding Agent. This is used to track Agents across simulation steps.
• action_mask is an optional list of two dimensional arrays of booleans which is only available when using multi-discrete actions. Each array corresponds to an action branch. The first dimension of each array is the batch size and the second contains a mask for each action of the branch. If true, the action is not available for the agent during this simulation step.

It also has the two following methods:

• len(DecisionSteps) Returns the number of agents requesting a decision since the last call to env.step().
• DecisionSteps[agent_id] Returns a DecisionStep for the Agent with the agent_id unique identifier.

A DecisionStep has the following fields:

• obs is a list of numpy arrays observations collected by the agent. (Each array has one less dimension than the arrays in DecisionSteps)
• reward is a float. Corresponds to the rewards collected by the agent since the last simulation step.
• agent_id is an int and an unique identifier for the corresponding Agent.
• action_mask is an optional list of one dimensional arrays of booleans which is only available when using multi-discrete actions. Each array corresponds to an action branch. Each array contains a mask for each action of the branch. If true, the action is not available for the agent during this simulation step.

TerminalSteps and TerminalStep

Similarly to DecisionSteps and DecisionStep, TerminalSteps (with s) contains information about a whole batch of Agents while TerminalStep (no s) only contains information about a single Agent.

A TerminalSteps has the following fields :

• obs is a list of numpy arrays observations collected by the group of agent. The first dimension of the array corresponds to the batch size of the group (number of agents requesting a decision since the last call to env.step()).
• reward is a float vector of length batch size. Corresponds to the rewards collected by each agent since the last simulation step.
• agent_id is an int vector of length batch size containing unique identifier for the corresponding Agent. This is used to track Agents across simulation steps.
• interrupted is an array of booleans of length batch size. Is true if the associated Agent was interrupted since the last decision step. For example, if the Agent reached the maximum number of steps for the episode.

It also has the two following methods:

• len(TerminalSteps) Returns the number of agents requesting a decision since the last call to env.step().
• TerminalSteps[agent_id] Returns a TerminalStep for the Agent with the agent_id unique identifier.

A TerminalStep has the following fields:

• obs is a list of numpy arrays observations collected by the agent. (Each array has one less dimension than the arrays in TerminalSteps)
• reward is a float. Corresponds to the rewards collected by the agent since the last simulation step.
• agent_id is an int and an unique identifier for the corresponding Agent.
• interrupted is a bool. Is true if the Agent was interrupted since the last decision step. For example, if the Agent reached the maximum number of steps for the episode.

BehaviorSpec

A BehaviorSpec has the following fields :

• observation_specs is a List of ObservationSpec objects : Each ObservationSpec corresponds to an observation's properties: shape is a tuple of ints that corresponds to the shape of the observation (without the number of agents dimension). dimension_property is a tuple of flags containing extra information about how the data should be processed in the corresponding dimension. observation_type is an enum corresponding to what type of observation is generating the data (i.e., default, goal, etc). Note that the ObservationSpec have the same ordering as the ordering of observations in the DecisionSteps, DecisionStep, TerminalSteps and TerminalStep.
• action_spec is an ActionSpec namedtuple that defines the number and types of actions for the Agent.

An ActionSpec has the following fields and properties:

• continuous_size is the number of floats that constitute the continuous actions.
• discrete_size is the number of branches (the number of independent actions) that constitute the multi-discrete actions.
• discrete_branches is a Tuple of ints. Each int corresponds to the number of different options for each branch of the action. For example: In a game direction input (no movement, left, right) and jump input (no jump, jump) there will be two branches (direction and jump), the first one with 3 options and the second with 2 options. (discrete_size = 2 and discrete_action_branches = (3,2,))

Communicating additional information with the Environment

In addition to the means of communicating between Unity and python described above, we also provide methods for sharing agent-agnostic information. These additional methods are referred to as side channels. ML-Agents includes two ready-made side channels, described below. It is also possible to create custom side channels to communicate any additional data between a Unity environment and Python. Instructions for creating custom side channels can be found here.

Side channels exist as separate classes which are instantiated, and then passed as list to the side_channels argument of the constructor of the UnityEnvironment class.

channel = MyChannel()

env = UnityEnvironment(side_channels = [channel])

Note : A side channel will only send/receive messages when env.step or env.reset() is called.

EngineConfigurationChannel

The EngineConfiguration side channel allows you to modify the time-scale, resolution, and graphics quality of the environment. This can be useful for adjusting the environment to perform better during training, or be more interpretable during inference.

EngineConfigurationChannel has two methods :

• set_configuration_parameters which takes the following arguments:
  • width: Defines the width of the display. (Must be set alongside height)
  • height: Defines the height of the display. (Must be set alongside width)
  • quality_level: Defines the quality level of the simulation.
  • time_scale: Defines the multiplier for the deltatime in the simulation. If set to a higher value, time will pass faster in the simulation but the physics may perform unpredictably.
  • target_frame_rate: Instructs simulation to try to render at a specified frame rate.
  • capture_frame_rate Instructs the simulation to consider time between updates to always be constant, regardless of the actual frame rate.
• set_configuration with argument config which is an EngineConfig NamedTuple object.

For example, the following code would adjust the time-scale of the simulation to be 2x realtime.

from mlagents_envs.environment import UnityEnvironment
from mlagents_envs.side_channel.engine_configuration_channel import EngineConfigurationChannel

channel = EngineConfigurationChannel()

env = UnityEnvironment(side_channels=[channel])

channel.set_configuration_parameters(time_scale = 2.0)

i = env.reset()
...

EnvironmentParameters

The EnvironmentParameters will allow you to get and set pre-defined numerical values in the environment. This can be useful for adjusting environment-specific settings, or for reading non-agent related information from the environment. You can call get_property and set_property on the side channel to read and write properties.

EnvironmentParametersChannel has one methods:

• set_float_parameter Sets a float parameter in the Unity Environment.
  • key: The string identifier of the property.
  • value: The float value of the property.

from mlagents_envs.environment import UnityEnvironment
from mlagents_envs.side_channel.environment_parameters_channel import EnvironmentParametersChannel

channel = EnvironmentParametersChannel()

env = UnityEnvironment(side_channels=[channel])

channel.set_float_parameter("parameter_1", 2.0)

i = env.reset()
...

Once a property has been modified in Python, you can access it in C# after the next call to step as follows:

var envParameters = Academy.Instance.EnvironmentParameters;
float property1 = envParameters.GetWithDefault("parameter_1", 0.0f);

Custom side channels

For information on how to make custom side channels for sending additional data types, see the documentation here.