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experiment.py
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from datetime import datetime
from typing import List, Tuple
from scripts.average import App
from data.data import AGENT_CLASS_TYPE, REP
class Experiment:
def __init__(self, iterations: int, combinations: List[Tuple[str, dict[str, object]]]) -> None:
self.iterations: int = iterations
self.combinations: List[Tuple[str, dict[str, object]]] = combinations
# create new average run config
def createConfig(self, agents: dict[str, object]) -> dict[str, object]:
return {
"agents": agents,
"enablePwrPlt": True,
"genomes": {
REP.BLINKY: ("saves", "ghost", 33),
REP.PINKY: ("saves", "ghost", 33),
},
"iterations": 1000,
"neuralnets": {
REP.PACMAN: ("saves", "pacman", 63),
REP.BLINKY: ("saves", "ghost", 35),
REP.PINKY: ("saves", "ghost", 35),
},
}
# run sub-experiment
def runPass(self, config: dict[str, object]) -> Tuple[float, int, int]:
return App(config).start(True)
# start experiment
def start(self) -> None:
filename: str = "./log/EXP{}.txt".format(datetime.now().strftime("%d%m_%H%M"))
open(filename, "x")
for set in self.combinations:
label: str = set[0]
agents: dict[str, object] = set[1]
avgC, avgT, avgD = self.runPass(self.createConfig(agents))
logFile = open(filename, "a")
logFile.write("{}: C[{}] T[{}] D[{}]\n".format(label, avgC, avgT, avgD))
logFile.close()
if __name__ == "__main__":
experiment: Experiment = Experiment(
[
# control run
# original implementation
(
"CTRL OGNL (OGNL / OGNL) [ VAR1 ]",
{
REP.PACMAN: AGENT_CLASS_TYPE.RAND,
REP.BLINKY: AGENT_CLASS_TYPE.OGNL,
"secondary": AGENT_CLASS_TYPE.RAND,
"randType": AGENT_CLASS_TYPE.OGNL,
},
),
# experiment 1:
# full aggressive
(
"EXP1 AGGR (AGGR / AGGR) [ VAR2 ]",
{
REP.PACMAN: AGENT_CLASS_TYPE.RAND,
REP.BLINKY: AGENT_CLASS_TYPE.AGGR,
"secondary": AGENT_CLASS_TYPE.RAND,
"randType": AGENT_CLASS_TYPE.AGGR,
},
),
# experiment 2:
# single ghost modification - one variant / one original
(
"EXP2 SMOD (AGGR / OGNL) [VAR2 -- VAR1]",
{
REP.PACMAN: AGENT_CLASS_TYPE.RAND,
REP.BLINKY: AGENT_CLASS_TYPE.AGGR,
"secondary": AGENT_CLASS_TYPE.RAND,
"randType": AGENT_CLASS_TYPE.OGNL,
},
),
(
"EXP2 SMOD (SMDP / OGNL) [VAR3 -- VAR1]",
{
REP.PACMAN: AGENT_CLASS_TYPE.RAND,
REP.BLINKY: AGENT_CLASS_TYPE.SMDP,
"secondary": AGENT_CLASS_TYPE.RAND,
"randType": AGENT_CLASS_TYPE.OGNL,
},
),
(
"EXP2 SMOD (GDQL / OGNL) [VAR4 -- VAR1]",
{
REP.PACMAN: AGENT_CLASS_TYPE.RAND,
REP.BLINKY: AGENT_CLASS_TYPE.GDQL,
"secondary": AGENT_CLASS_TYPE.RAND,
"randType": AGENT_CLASS_TYPE.OGNL,
},
),
(
"EXP2 SMOD (NEAT / OGNL) [VAR5 -- VAR1]",
{
REP.PACMAN: AGENT_CLASS_TYPE.RAND,
REP.BLINKY: AGENT_CLASS_TYPE.NEAT,
"secondary": AGENT_CLASS_TYPE.RAND,
"randType": AGENT_CLASS_TYPE.OGNL,
},
),
# experiment 3:
# double ghost modification
(
"EXP3 DMOD (AGGR / SMDP) [VAR2 -- VAR3]",
{
REP.PACMAN: AGENT_CLASS_TYPE.RAND,
REP.BLINKY: AGENT_CLASS_TYPE.AGGR,
"secondary": {
REP.INKY: AGENT_CLASS_TYPE.NONE,
REP.CLYDE: AGENT_CLASS_TYPE.NONE,
REP.PINKY: AGENT_CLASS_TYPE.SMDP,
},
},
),
(
"EXP3 DMOD (AGGR / GDQL) [VAR2 -- VAR4]",
{
REP.PACMAN: AGENT_CLASS_TYPE.RAND,
REP.BLINKY: AGENT_CLASS_TYPE.AGGR,
"secondary": {
REP.INKY: AGENT_CLASS_TYPE.NONE,
REP.CLYDE: AGENT_CLASS_TYPE.NONE,
REP.PINKY: AGENT_CLASS_TYPE.GDQL,
},
},
),
(
"EXP3 DMOD (AGGR / NEAT) [VAR2 -- VAR5]",
{
REP.PACMAN: AGENT_CLASS_TYPE.RAND,
REP.BLINKY: AGENT_CLASS_TYPE.AGGR,
"secondary": {
REP.INKY: AGENT_CLASS_TYPE.NONE,
REP.CLYDE: AGENT_CLASS_TYPE.NONE,
REP.PINKY: AGENT_CLASS_TYPE.NEAT,
},
},
),
(
"EXP3 DMOD (SMDP / GDQL) [VAR3 -- VAR4]",
{
REP.PACMAN: AGENT_CLASS_TYPE.RAND,
REP.BLINKY: AGENT_CLASS_TYPE.SMDP,
"secondary": {
REP.INKY: AGENT_CLASS_TYPE.NONE,
REP.CLYDE: AGENT_CLASS_TYPE.NONE,
REP.PINKY: AGENT_CLASS_TYPE.GDQL,
},
},
),
(
"EXP3 DMOD (SMDP / NEAT) [VAR3 -- VAR5]",
{
REP.PACMAN: AGENT_CLASS_TYPE.RAND,
REP.BLINKY: AGENT_CLASS_TYPE.SMDP,
"secondary": {
REP.INKY: AGENT_CLASS_TYPE.NONE,
REP.CLYDE: AGENT_CLASS_TYPE.NONE,
REP.PINKY: AGENT_CLASS_TYPE.NEAT,
},
},
),
(
"EXP3 DMOD (GDQL / NEAT) [VAR4 -- VAR5]",
{
REP.PACMAN: AGENT_CLASS_TYPE.RAND,
REP.BLINKY: AGENT_CLASS_TYPE.GDQL,
"secondary": {
REP.INKY: AGENT_CLASS_TYPE.NONE,
REP.CLYDE: AGENT_CLASS_TYPE.NONE,
REP.PINKY: AGENT_CLASS_TYPE.NEAT,
},
},
),
]
)
experiment.start()