I can't get pool.map or pool.starmap to work the way I think it should - parallel-processing

Question

So for the life of me I can't figure out which combination of pool.map() or pool.starmap() should be used to make this work as I want. The code below executes correctly, firing off (sequentially!) two instances of runSim and appending the results.
results = []
argsToRun = []
for n in range(2):
random.seed()
env = simpy.Environment() # Create the SimPy environment
env.clockRate = 1e9
argsToRun.append([env, adist, sdist, tdist, 108, intSpeed, runUntil])
for arg_list in argsToRun:
line = runSim(*arg_list)
results.append(line)
I was under the impression that something like the code below would be a simple way to run these instances in parallel (the results are not order-dependent)... but neither of them seem to work. Surely I'm missing something obvious?
with multiprocessing.Pool() as pool:
results = pool.map(runSim, argsToRun)
pool.close()
Nor....
with multiprocessing.Pool() as pool:
results = pool.starmap(runSim, argsToRun)
pool.close()

Answer 1

After lots of frustration and searching, it turns out that some of my parameters were not pickleable. This, combined with a bug in VSCode which prevented the debugger from running made it much harder to diagnose than it should have been.
Switching to the pathos versions of Pool() solved the problem.
for n in range(15):
random.seed()
env = simpy.Environment() # Create the SimPy environment
argsToRun.append((env, adist, sdist, tdist, 108, intSpeed, runUntil))
with pathos.helpers.mp.Pool as pool: ## tada!
results = pool.starmap(runSim, argsToRun)