- rlFiniteSetSpec: Use this specification when the action space consists of a discrete set of possible actions. Each action in the set can be represented by a unique value or identifier. For example, if your action space consists of a finite set of discrete actions, such as "move forward," "turn left," or "turn right," you can use rlFiniteSetSpec to define the action space.
- rlNumericSpec: Use this specification when the action space is continuous or can be represented by a range of numerical values. If your action space requires specifying a continuous value, such as controlling the speed or position of a robotic arm, you can use rlNumericSpec to define the action space.
Mixed DataType in action space
3 vues (au cours des 30 derniers jours)
For mixed type of Data in action space, which of them will be used rlFiniteSetSpec or rlNumericSpec. I have seen a query regarding the observation space, but does it holds for action space too?
Shaik le 13 Mai 2023
The choice between rlFiniteSetSpec and rlNumericSpec for the action space depends on the nature of the action you want to define in your reinforcement learning (RL) environment.
When defining the action space in an RL environment, you need to choose the appropriate specification based on the characteristics of the actions you want to represent. You can have a mix of rlFiniteSetSpec and rlNumericSpec objects within the action space if your environment requires both discrete and continuous actions.
Regarding the link you provided, it refers to a query about the observation space, but the same concept applies to the action space as well. The rlFiniteSetSpec and rlNumericSpec objects can be used to define both observation and action spaces in an RL environment, depending on the type of data they represent.
In summary, consider the nature of your action space (discrete or continuous) and use either rlFiniteSetSpec or rlNumericSpec accordingly to define the appropriate specification for your action space in the RL environment.