Action space
The fundamental and obvious difference with a continuous action space is its continuity. In contrast to a discrete action space, when the action is defined as a discrete mutually exclusive set of options to choose from, the continuous action has a value from some range. On every time step, the agent needs to select the concrete value for the action and pass it to the environment.
In Gym, a continuous action space is represented as the gym.spaces.Box class, which was described in Chapter 2,OpenAI Gym, when we talked about the observation space. You may remember that Box includes a set of values with a shape and bounds. For example, every observation from the Atari emulator was represented as Box(low=0, high=255, shape=(210, 160, 3)), which means 100,800 values organized as a 3D tensor, with values from the 0..255 range.
For the action space, it's unlikely that you'll work with such large numbers of actions. For example, the robot that we'll use as...
