Regression through reinforcement learning
In order to output the correct power and angle, all you need to do is go into your neural network architecture and change the activation of your last layer.  
In your question, you stated that you are currently using an action classification output, so it is most likely a softmax output layer.  We can do two things here:

If the power and angle has hard constraints, e.g. the angle cannot be greater than 360°, or the power cannot exceed 700 kW, we can change the softmax output to a TanH output (hyperbolic tangent) and multiply it by the constraint of power/angle.  This will create a "scaling effect" because tanh's output is between -1 and 1.  Multiplying the tanh's output by the constraint of the power/angle ensures that the constraints are always satisfied and the output is the correct power/angle.
If there are no constraints on your problem.  We can simply just delete the softmax output all together.  Removing the softmax allows for the output to no longer be constrained between 0 and 1.  The last layer of the neural network will simply act as a linear mapping, i.e., y = Wx + b. 

I hope this helps!
EDIT: In both cases, your reward function to train your neural network can simply be a MSE loss.  Example: loss = (real_power - estimated_power)^2 + (real_angle - estimated_angle)^2
I'm trying to build an Agent that can play Pocket Tanks using RL. The problem I'm facing now is that how can I train a neural network to output the correct Power and Angle. so instead of actions classification. and I want a regression.