Python Agent.setLocation方法代码示例

# 需要导入模块: from agent import Agent [as 别名]
# 或者: from agent.Agent import setLocation [as 别名]
def valueIteration(discountFactor):
	# all locations in grid
	alllocations = [ (x,y) for x in range(11) for y in range(11)]

	# initialize values
	values = {}
	bestMoves = {}
	for predloc in alllocations:
			for preyloc in alllocations:
				if preyloc != predloc:
					values[(predloc,preyloc)] = 0

	agent = Agent(0,0)

	deltas = []
	epsilon = 0.01
	delta = 1
	numIt = 0
	# perform value iteration according to pseud-code
	while delta > epsilon:
		delta = 0
		newValues = {}
		# loop over all states
		for predloc in alllocations:
			for preyloc in alllocations:
				if predloc == preyloc:
					continue
				agent.setLocation(predloc)
				prey = Prey(*preyloc)
				temp = values[(predloc,preyloc)]
				# find optimal value according to current values
				bestVal = 0
				bestMove = (0,0)
				for prob, predMove in agent.getMoveList():
					preySum = 0
					newPredloc = ((predloc[0] + predMove[0])%11,(predloc[1] + predMove[1])%11)
					if newPredloc == preyloc :
						preySum += 10.0
					else:
						for preyProb, newPreyloc in prey.expand(newPredloc):
							preySum += preyProb * discountFactor * values[(newPredloc,newPreyloc)]
					if bestVal <= preySum:
						bestVal = preySum
						bestMove = predMove
				newValues[(predloc,preyloc)] = bestVal
				bestMoves[(predloc,preyloc)] = bestMove
				delta = max(delta, np.abs(bestVal - temp))
		values = newValues
		deltas.append(delta)
		numIt+=1
	# greedy policy to the optimal values computed above
	def policy(state):
		predloc, preyloc = state
		agent.setLocation(predloc)
		prey = Prey(*preyloc)
		return bestMoves[(predloc,preyloc)]
	return numIt, values, policy

# 需要导入模块: from agent import Agent [as 别名]
# 或者: from agent.Agent import setLocation [as 别名]
def valueIteration():

	alldiffs = [ (x,y) for x in range(-5,6) for y in range(-5,6)]
	alldiffs.remove((0,0))

	# the relative positions vary from -5 up to 5, in both dimensions
	values = {}
	for x in range(-5,6):
		for y in range(-5,6):
			values[(x,y)] = 0

	bestMoves = {}
	agent = Agent(0,0)

	deltas = []
	discountFactor = 0.8
	epsilon = 0.01
	delta = 1
	while delta > epsilon:
		delta = 0
		newValues = {}
		for diff in alldiffs:
			# we place the predator in the middle of the world,
			# we are allowed to do this, since the positions are encoded relatively
			predloc = (5,5)
			preyloc = (predloc[0]+diff[0],predloc[1]+diff[1])
			curKey  = rewriteStates(predloc,preyloc)
			agent.setLocation(predloc)
			prey = Prey(*preyloc)
			temp = values[curKey]
			bestVal = 0
			bestMove = (0,0)
			for prob, predMove in agent.getMoveList():
				preySum = 0
				newPredloc = agent.locAfterMove(predMove)
				if newPredloc == preyloc :
					preySum += 10.0
				else:
					for preyProb, newPreyloc in prey.expand(newPredloc):
						# using rewriteStates we use relative positions
						preySum += preyProb * discountFactor * values[rewriteStates(newPredloc,newPreyloc)]
				if bestVal <= preySum:
					bestVal = preySum
					bestMove = predMove
			newValues[curKey] = bestVal
			bestMoves[curKey] = bestMove
			delta = max(delta, np.abs(bestVal - temp))
		values = newValues
		deltas.append(delta)

	def policy(state):
		predloc, preyloc = state
		agent.setLocation(predloc)
		prey = Prey(*preyloc)
		return bestMoves[rewriteStates(predloc,preyloc)]
	return policy

# 需要导入模块: from agent import Agent [as 别名]
# 或者: from agent.Agent import setLocation [as 别名]
def valueFunction():
	# all locations on the grid
	alllocations = [ (x,y) for x in range(11) for y in range(11)]

	# initialize value function
	values = {}
	for predloc in alllocations:
		for preyloc in alllocations:
			if preyloc != predloc:
				values[(predloc,preyloc)] = 0

	# predator which is placed in the top-left
	agent = Agent(0,0)

	discountFactor = 0.8
	epsilon = 0.01
	delta = 1
	numIt = 0
	while delta > epsilon:
		delta = 0
		newValues = {}
		# sweep over all possible states
		for predloc in alllocations:
			for preyloc in alllocations:
				if predloc == preyloc:
					continue
				# place predator and prey at location
				agent.setLocation(predloc)
				prey = Prey(*preyloc)
				# temp is previous value of state
				temp = values[(predloc,preyloc)]
				moveSum = 0
				# iterates over each actionthe agent can take
				# and the probability of the action according to the policy
				for prob, newPredloc in agent.expand():
					preySum = 0
					# absorbing state
					if newPredloc == preyloc :
						preySum += 10.0
					else:
						# iterates over the states which the action can lead to, and their probability (stochastic)
						for preyProb, newPreyloc in prey.expand(newPredloc):
							# part of update rule (sum over s')
							preySum += preyProb * discountFactor * values[(newPredloc,newPreyloc)]
					# part of update rule (sum over a)
					moveSum += prob * preySum
				# policy evaluation update
				newValues[(predloc,preyloc)] = moveSum
				delta = max(delta, np.abs(moveSum - temp))
		values = newValues
		numIt += 1

	return values, numIt