using Q learning agent for continuous observation space
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I have a reinforcement learning problem where the observation is the error of closed loop feedback and it is continuous, and discrete action space.
I want to use Q learning as its document tells It can handle both discrete/continuous observations.
but Im a little bit confused about making critic using rlQValueRepresentation which its syntax mostly uses either a table or deep neural network,
and they are inappropriate for my work, as I didnt find any example like this in Mathworks website, Is there anyone who can help me on this?
Stephan on 16 Jun 2020
You also are allowed to write a custom critic function: