Most reinforcement learning research papers focus on environments where the agent’s actions are either discrete or continuous. However, when training an agent to play a video game, it is common to encounter situations where actions have both discrete and continuous components. For example, a set of high-level discrete actions (ex: move, jump, fire), each of them being associated with continuous parameters (ex: target coordinates for the move action, direction for the jump action, aiming angle for the fire action). These kinds of tasks are included in Control with Parameterised Actions.
