evaluatePolicy.m output differs from Agent action

Dear Mathworks Team,

I have a Matlab defined RL-algorithm with an DDPG-agent which recieves two observations can chose values between 0 and 1.

This is defined through:

numAct = 1;

actionInfo = rlNumericSpec([numAct 1],'LowerLimit',0 ,'UpperLimit', 1);

actionInfo.Name = 'sine_amplitude';

During training only values between 0 and 1 are applied. The action is clipped at those values and the actionInfo is repected.

However when I use the generated Agent to generate a Policy according to Matlab (see https://www.mathworks.com/help/reinforcement-learning/ref/rl.agent.rldqnagent.generatepolicyfunction.html)

and evaluate the function I also recieve negative values.

For example

evaluatePolicy(reshape([-0.1515581,1],2,1,1))

returns -1

I have used reshape-functions to reshape the data [-0.1515581,1] to the corresponding shape since the function expects the input to be of shape (2,1,1).

My question is why and how can i change generation of the evaluatePolicy function?