How do I define a continuous reward function for RL environment?

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I am trying to follow the double integrator example for giving a continuous reward function. When I used the custom template, and defined the reward using the QR cost function, I get an error stating that the reward should be a scalar value. Where can I find the property of reward and change it to accept vector values?
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Prashanth Chivkula
Prashanth Chivkula on 12 Oct 2020
Yes I did that, thank you, Just to confirm the output of the cost function will always be a scalar value, right? So in the double integrator continuous example there are two states but the output reward at each step is a scalar value, right?

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Accepted Answer

Priysha LNU
Priysha LNU on 8 Oct 2020
Here is an excerpt from the documentation :
To guide the learning process, reinforcement learning uses a scalar reward signal generated from the environment.
For detailed information on defining reward signals, discrete and continous rewards, please refer to this documentation link.

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