Not sure about SetReward and AddReward functionality

Hey guys,
Just asking a simple question about SetReward and AddReward in regards to Reinforcement Learning functionality.

So obviously, every time-step (or after a batched amount of timesteps), the NN receives state+reward at each timestep t.
If I add reward 0.1 at timestep 0 and then addreward 0.1 at timestep 1, is it like: reward t_0 = 0.1, reward t_1 = 0.2? So the cumulative reward would be 0.3?
So if i SetReward 0.1 at t_0 and at t_1, would my cumulative reward be 0.2?

Does this mean, if I want to add a negative reward of -0.1 at every time-step, I should do this with SetReward(-0.1), instead of AddReward(-0.1), since AddReward would actually not result in a linear increase of reward, but actually exponential?

Also, should I set the reward in the

CollectObservations

or

OnActionReceived

function? Shouldn't there simply be an additional function for this?

 

@andrewcoh_unity Thanks for the reply!
Is a "timestep" defined as the DecisionPeriod set in the DecisionRequester?
Where one step in the set period would be a single EnvironmentStep call on the academy (i.e. fixedupdate per default)?

Also, I don't quite understand how taking actions between decisions works - isn't the definition of a decision deciding on the next action to do (i.e. one input->output run through the Python interface)? How is the action vector decided upon betwen decisions?

What do you mean with *Corresponding to max step*?

My current understanding of the academy flow is:
- Setup environment, connect with python, etc.
Loop:
- EnvironmentStep is called
- If there is no episode running, start a new one
- Once EnvironmentStep count is >= decision period, continue
- CollectObservations is called, "consuming" the reward up till now, setting it back to 0
- The received observations and rewards are sent to the python interface to go through the NN
- Wait for the return of action vectors from python
- Call OnActionReceived with the actions just received
- wait for the next environmentstep and loop

And somewhere inbetween batches, the Python interface does some backpropagation for training, this is also where freezes occur while training, since we have to wait for this to finish before requesting the next action.

Is something wrong in my assumptions or did I miss something?