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(
numTakenActions, episode)
agent.setEpsilon(epsilon)
agent.setLearningRate(learningRate)
obsCopy = observation.copy()
agent.setState(agent.toStateRepresentation(obsCopy))
action = agent.act()
numTakenActions += 1
nextObservation, reward, done, status = hfoEnv.step(action)
agent.setExperience(agent.toStateRepresentation(obsCopy), action,
reward, status,
agent.toStateRepresentation(nextObservation))
update = agent.learn()
observation = nextObservation
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()
break
