rpy(2) does not convert code. It only allows you an interface to communicate with R from python and issue R commands from within python.

Given that R is very dependent upon statistical libraries that aren't available in python the conversion would generally be a tremendous amount of work. To convert a single line like

lmer (y ~ x1 + x1 + (1|id), mydat)

to something in python would take a great deal of work. If you just want to run that code from python use rpy. Going the other way, which seems to be more what you are interested in, the conversion could often be a bit more straightforward. However, the code would tend to be very poorly optimized for R and run very badly. I recently optimized some R code converted from python by someone who wrote the R code as a more or less direct translation line by line. The optimization came out 1/3 the length and between 20 and 1000 times faster (depending on the function).

This tells you everything you need to know about using reticulate

Interface to 'Python' • reticulate

It doesn't convert or translate your Python code to R code, rather it runs your a Python session within and R session. Read the linked page for what this allows you to do and how to do it.

You have a couple of problems.

1. You are zero-indexing your vectors. R is 1-indexed (first element of y is y[1] not y[0].
2. Ranges (slices in python) in R are inclusive. Eg: 0:2 = c(0, 1, 2) while python is right-exclusive 0:2 = [0, 1].
3. R uses minus elements to "remove" elements of vectors, while Python uses these to extract from reverse order. Eg: y[-1] = y[2:length(y)] in R.
4. R's range function is not the same as Python's range function. The equivalent in R would be seq or a:b (example 3:n). Not again that it is right-inclusive while pythons is right-exclusive!
5. You are not storing your intermediary results in a as you are doing in python. You need to do this at run-time

And last: R functions will return the last evaluation by default. So there is no need to explicitly use return. This is not a problem per-say, but something that can make code look cleaner (or less clean in some cases). So one option to fix you problem would be:

robber <- function(nums){
  n <- length(nums) # <= Only compute length **once** =>
  if(n == 0)
    0 # <= Returned because no more code is run after this =>
  else if(n <= 2)
    max(nums) # <= Returned because no more code is run after this =>
  else{
    a <- numeric(n) # <= pre-allocate our vector =>
    a[1:2] <- cummax(nums[1:2]) # <= Cummax instead of c(nums[1], max(nums[1:2])) =>
    for(i in 3:n){ # <= Note that we start at 3, because of R's 1-indexing =>
      a[i] <- max(a[i - 1], a[i - 2] + nums[i])
    }
    a[n]
  }
}

Note 3 things:

1. I use that R vectors are 1-indexed, and my range goes from 3 as a consequence of this.
2. I pre-allocate my a vector (here using numeric(n)). R vector expansion is slow while python lists are constant in time-complexity. So preallocation is the recommended way to go in all cases.
3. I extract my length once and store it in a variable. n <- length(nums). It is inherently unnecessary to evaluate this multiple times, and it is recommended to store these intermediary results in a variable. This goes for any language such as R, Python and even in compild languages such as C++ (while for the latter, in many cases the compiler is smart enough to not recompute the result).

Last I use cummax where I can. I feel there is an optimized way to get your result almost immediately using vectorization, but I can't quite see it.