def __init__(self, discountFactor, epsilon, initVals=0.0):
super(MonteCarloAgent, self).__init__()
self.attack = HFOAttackingPlayer()
# initialise all possible states for the agent
self.State = [(x, y) for x in range(5) for y in range(6)]
self.State.append("GOAL")
self.State.append("OUT_OF_BOUNDS")
# discount
self.discountFactor = discountFactor
# epsilon
self.epsilon = epsilon
# variable current state
self.cur = 0
# 3 empty lists used to record the episode
self.logA = []
self.logR = []
self.logS = []
# cumulative reward
self.G = 0
# Q table is a dict where keys are the "states" and values are another dict.
# This inside dict contains "actions" as keys and the values are initilised to 0
self.Q = {}
# Returns table is a dict where the key is "state-action" pairs
self.returns = {}
for s in self.State:
self.Q[s] = {}
for a in self.possibleActions:
self.Q[s][a] = 0
self.returns[(s, a)] = []
return self.learningRate, self.epsilon
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--id', type=int, default=0)
parser.add_argument('--numOpponents', type=int, default=0)
parser.add_argument('--numTeammates', type=int, default=0)
parser.add_argument('--numEpisodes', type=int, default=500)
args = parser.parse_args()
# Initialize connection with the HFO server
hfoEnv = HFOAttackingPlayer(numOpponents=args.numOpponents,
numTeammates=args.numTeammates,
agentId=args.id)
hfoEnv.connectToServer()
# Initialize a Q-Learning Agent
agent = QLearningAgent(learningRate=0.1, discountFactor=0.99, epsilon=1.0)
numEpisodes = args.numEpisodes
# Run training using Q-Learning
numTakenActions = 0
for episode in range(numEpisodes):
status = 0
observation = hfoEnv.reset()
while status == 0:
learningRate, epsilon = agent.computeHyperparameters(
from DiscreteHFO.HFOAttackingPlayer import HFOAttackingPlayer
import random
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--id', type=int, default=0)
parser.add_argument('--numOpponents', type=int, default=0)
parser.add_argument('--numTeammates', type=int, default=0)
parser.add_argument('--numEpisodes', type=int, default=500)
args=parser.parse_args()
hfoEnv = HFOAttackingPlayer(numOpponents = args.numOpponents, numTeammates = args.numTeammates, agentId = args.id)
hfoEnv.connect_to_server()
numEpisodes = 500
for episode in range(numEpisodes+1):
status = 0
observation = hfoEnv.reset()
while status==0:
act = random.randint(0,4)
nextObservation, reward, done, status = hfoEnv.step(hfoEnv.possibleActions[act])
observation = nextObservation
if status == 5:
hfoEnv.quitGame()
