# 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
  Policy.
* **Policy** - The decision making mechanism, typically a neural network model.
* **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 and the Academy.
* **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** - 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
  group of Agents.