In Reinforcement Learning, an action space is the set of all possible actions an agent can take in a given environment.
A continuous action space is one where the action space is represented by a continuous set of values. In other words, an agent can take an infinite number of possible actions within a specific range. One common example of a continuous action space is controlling the speed of a self-driving car.
For instance, let’s say that the agent is a self-driving car that needs to navigate through traffic. The agent’s action space could be to control its speed. The agent can choose to increase or decrease its speed by any amount within a specific range, say between 0 to 100 km/hr.
Instead of selecting an action from a discrete set of actions, such as ’accelerate’, ’maintain speed’ and ’decelerate’, the agent can choose the amount it wishes to adjust its speed within the permitted range. The agent needs to learn to balance speed, braking, and lane changing to successfully navigate the traffic and reach its destination as quickly and safely as possible.
In such scenarios, the agent’s action space is continuous, allowing it to operate on a fine-grained level, which in turn can result in more precise and effective control.