def run(episodeNum):
episodeNum = int(episodeNum)
setupMultithreading()
results = multiprocessing.Queue()
finished = multiprocessing.Queue()
processes = []
for _ in range(localConstants.REPEATS):
for percentage in np.linspace(0.1, 1.0, num=10):
processes.append(
multiprocessing.Process(target=threadRun,
args=(percentage, episodeNum, results,
finished)))
results = executeMulti(processes,
results,
finished,
numResults=episodeNum * len(processes))
# plotting.plotMulti("Competing Agents", results=results, ylabel="Job #", xlabel="Episode #") # , save=True)
plotting.plotMultiWithErrors("Competing Agents",
results=results,
ylabel="Job #",
xlabel="Episode #") # , save=True)
localConstants.REPEATS = 16
numEpisodes = int(numEpisodes)
# , ]minimalTableAgent
systemState = extendedSystemState
# agentsToTest = [minimalTableAgent, randomAgent] # minimalTableAgent, , localAgent]
numPhases = int(3e1)
for agent, offPolicy in [(minimalDeepAgent, True), (minimalDeepAgent, False), (minimalTableAgent, False)]:
for production in [True, False]:
for pretrain in [True, False]:
for _ in range(localConstants.REPEATS):
for centralised in [True]:
# if not (not centralised and agent is randomAgent):
processes.append(multiprocessing.Process(target=runThread, args=(
len(processes), agent, systemState, production, offPolicy, pretrain, numPhases, numEpisodes, results, finished)))
results = executeMulti(processes, results, finished, numResults=len(
processes) * numPhases * numEpisodes, assembly=assembleResults, chooseBest=1.0)
plotting.plotMultiWithErrors("experiment6", title="experiment 6", results=results, ylabel="Job #", xlabel="Episode #") # , save=True)
if __name__ == "__main__":
setupMultithreading()
try:
run(1e2)
except:
traceback.print_exc(file=sys.stdout)
print("ERROR")
