ML-Agents Toolkit Glossary

• Academy - Singleton object 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 Policy.
• Decision - The specification produced by a Policy 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.
• Experience - Corresponds to a tuple of [Agent observations, actions, rewards] of a single Agent obtained after a Step.
• External Coordinator - ML-Agents class responsible for communication with outside processes (in this case, the Python API).
• FixedUpdate - Unity method called each time 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)
• Policy - The decision-making mechanism for producing decisions from observations, typically a neural network model.
• 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 an atomic change of the engine that happens between Agent decisions.
• Trainer - Python class which is responsible for training a given group of Agents.
• Update - Unity function called each time a frame is rendered. ML-Agents logic should not be placed here.