ml-agents/docs/Python-API.md

Python API

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 python/unityagents. To load a Unity environment from a built binary file, put the file in the same directory as unityagents. If your filename is 3DBall.app, in python, run:

from unityagents import UnityEnvironment
env = UnityEnvironment(file_name="3DBall", worker_id=0)

• 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.

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^th observation of the brain.
• vector_observations : A two dimensional numpy array of dimension (batch size, vector observation size) if the vector observation space is continuous and (batch size, 1) if the vector observation space is discrete.
• 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 (wether 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, 1) if the vector action space is discrete.

Once loaded, env 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 more information on adding optional config flags to an environment, see here. 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.
• 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.

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:

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.