Hi Anna,
The error message you are encountering indicates a mismatch between the number of input layers in your critic network and the number of observation channels provided by your environment's observation specifications. Referring to the Biped Robot example as here: https://www.mathworks.com/help/reinforcement-learning/ug/train-biped-robot-to-walk-using-reinforcement-learning-agents.html#TrainBipedRobotToWalkUsingReinforcementLearningAgentsExample-2 ,
the dimension of ObservationInfo of the environment is [29 1], while the input layer of your critic network expects an image with the dimension [31 1 1]. The use of imageInputLayer is typically for image data inputs. If your observations and actions are not image data (which seems to be the case given their dimensions), consider using featureInputLayer instead for a more appropriate representation. This is especially true if your observations and actions are simply vectors of numerical values.
You can adjust the observation input layer as shown below:
statePath = [
featureInputLayer(obsInfo.Dimension(1),'Normalization','none','Name', 'observation')
% ... rest of your layers
];
For the action layer, you should use rlDiscreteCategoricalActor function as the action space is discrete for the environment. You can read more about the function here: https://www.mathworks.com/help/reinforcement-learning/ref/rl.function.rldiscretecategoricalactor.html
Refer to the following link to know the process of creating critic and actor network using rlvalueFunction: https://www.mathworks.com/help/reinforcement-learning/ref/rl.function.rlvaluefunction.html#mw_810cde6a-16f5-47da-b472-acabe4917d18
