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ML-Agents Toolkit Glossary

  • Academy - Unity Component which controls timing, reset, and training/inference settings of the environment.
  • Action - The carrying-out of a decision on the part of an agent within the environment.
  • Agent - Unity Component which produces observations and takes actions in the environment. Agents actions are determined by decisions produced by a linked Brain.
  • Brain - Unity Asset which makes decisions for the agents linked to it.
  • Decision - The specification produced by a Brain for an action to be carried out given an observation.
  • Editor - The Unity Editor, which may include any pane (e.g. Hierarchy, Scene, Inspector).
  • Environment - The Unity scene which contains Agents, Academy, and Brains.
  • FixedUpdate - Unity method called each time the the game engine is stepped. ML-Agents logic should be placed here.
  • Frame - An instance of rendering the main camera for the display. Corresponds to each Update call of the game engine.
  • Observation - Partial information describing the state of the environment available to a given agent. (e.g. Vector, Visual, Text)
  • Policy - Function for producing decisions from observations.
  • Reward - Signal provided at every step used to indicate desirability of an agent’s action within the current state of the environment.
  • State - The underlying properties of the environment (including all agents within it) at a given time.
  • Step - Corresponds to each FixedUpdate call of the game engine. Is the smallest atomic change to the state possible.
  • Update - Unity function called each time a frame is rendered. ML-Agents logic should not be placed here.
  • External Coordinator - ML-Agents class responsible for communication with outside processes (in this case, the Python API).
  • Trainer - Python class which is responsible for training a given Brain. Contains TensorFlow graph which makes decisions for Learning Brain.