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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Emmanouil Tzorakoleftherakis
2020-10-12
Not sure why you want the reward to be scalar. Typically, rewards are treated as cost functions - they output a scalar value. If you have more than one states, you can turn it into a scalar using e.g. an l2 norm for example/some distance metric.
Prashanth Chivkula
2020-10-12
Emmanouil Tzorakoleftherakis
2020-10-12
That's right
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Priysha LNU
2020-10-8
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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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