def main(argv):
# queens = createBoard(argv[0])
queens = getDemoBoard(argv[0])
heur = 0 if argv[2] == "H1" else 1
if int(argv[1]) == 1: # A* Algorithm
astar_solver = AStar(queens, heur)
print(astar_solver.Solve())
elif int(argv[1]) == 2: # Hill Climb
hill_solver = HillClimb(queens, heur)
hill_solver.solve()
class Experiment:
def __init__(self, real):
self.Real = real
self.ASTAR = AStar(self.Real.XSize, self.Real.YSize,
self.Real.Occlusions)
self.PRQL = PRQL(self.Real)
def Run(self, policy):
undiscountedReturn = 0.0
discountedReturn = 0.0
discount = 1.0
state = self.Real.CreateStartState()
currentState = self.Real.Copy(state)
t = 0
while True:
try:
action = policy[t]
except IndexError:
self.ASTAR.__init__(self.Real.XSize, self.Real.YSize,
self.Real.Occlusions)
self.ASTAR.InitializeGrid((state.AgentPos).Copy(),
(self.Real.GoalPos).Copy())
path = self.ASTAR.Solve()
pathPos = COORD(path[1][0], path[1][1])
for action in range(self.Real.NumActions):
agentPos = (state.AgentPos).Copy()
nextAgentPos = self.Real.NextPos(agentPos, action)
if nextAgentPos == pathPos:
break
terminal, state, observation, reward = self.Real.Step(
state, action)
currentState = self.Real.Copy(state)
undiscountedReturn += reward
discountedReturn += reward * discount
discount *= self.Real.GetDiscount()
if terminal:
return reward
t += 1
return None
def DiscountedReturn(self, policies, predatorHome, occlusions):
if not policies:
return 0.0
self.Real.__init__(self.Real.XSize,
self.Real.YSize,
occlusions=occlusions)
self.Real.PredatorHome = predatorHome
self.PRQL.__init__(self.Real, policies=policies)
newPolicyLibrary = self.PRQL.RemoveDuplicates(policies)
self.PRQL.PolicyLibrary = newPolicyLibrary
survivalrates = 0
trajGraphDistances = []
for j in range(2):
terminalRewards = []
successTrajectoryIndices = []
for i in range(self.PRQL.N):
self.PRQL.PolicyIndex = self.PRQL.ChoosePolicy()
policy = self.PRQL.PolicyLibrary[self.PRQL.PolicyIndex]
terminalReward = self.Run(policy)
if not terminalReward:
i -= 1
continue
terminalRewards.append(terminalReward)
if terminalReward > 0:
successTrajectoryIndices.append(self.PRQL.PolicyIndex)
survivalrates += float(
sum(reward > 0 for reward in terminalRewards)) / float(
len(terminalRewards))
trajGraphDistances.append(
self.PRQL.gDistance(successTrajectoryIndices))
return survivalrates / 2., np.mean(trajGraphDistances)
class Simulator:
def __init__(self,
actions,
observations,
goal,
occlusions,
discount=1.0,
xsize=15,
ysize=15):
self.NumActions = actions
self.NumObservations = observations
self.Discount = discount
self.Knowledge = Knowledge()
self.RewardRange = 100
self.GoalPos = goal
self.XSize = xsize
self.YSize = ysize
self.Occlusions = occlusions
self.ASTAR = AStar(self.XSize, self.YSize, self.Occlusions)
assert (self.Discount > 0 and self.Discount <= 1.0)
def Validate(self, state):
return True
def LocalMove(self, state, history, stepObs, status):
return 1
def GenerateLegal(self, state, history, actions, status):
for a in range(self.NumActions):
actions.append(a)
return actions
def GeneratePreferred(self, state, history, actions, status):
return actions
def SelectRandom(self, state, history, status):
if self.Knowledge.RolloutLevel >= MOVES.SMART:
actions = []
actions = self.GeneratePreferred(state, history, actions, status)
if actions:
return actions[Random(0, len(actions))]
if self.Knowledge.RolloutLevel >= MOVES.LEGAL:
actions = []
actions = self.GenerateLegal(state, history, actions, status)
if actions:
return actions[Random(0, len(actions))]
return Random(0, self.NumActions)
def SelectASTARRandom(self, state, history, status):
if Bernoulli(0.5):
self.ASTAR.__init__(self.XSize, self.YSize, self.Occlusions)
self.ASTAR.InitializeGrid((state.AgentPos).Copy(),
self.GoalPos.Copy())
path = self.ASTAR.Solve()
pathPos = COORD(path[1][0], path[1][1])
for action in range(self.NumActions):
agentPos = (state.AgentPos).Copy()
nextAgentPos = agentPos + Compass[action]
if nextAgentPos == pathPos:
break
return action
return self.SelectRandom(state, history, status)
def Prior(self, state, history, vnode, status):
actions = []
if self.Knowledge.TreeLevel == MOVES.PURE:
vnode.SetChildren(0, 0)
else:
vnode.SetChildren(LargeInteger, -Infinity)
if self.Knowledge.TreeLevel >= MOVES.LEGAL:
actions = []
actions = self.GenerateLegal(state, history, actions, status)
for action in actions:
qnode = vnode.Child(action)
qnode.Value.Set(0, 0)
qnode.AMAF.Set(0, 0)
vnode.Children[action] = qnode
if self.Knowledge.TreeLevel >= MOVES.SMART:
actions = []
actions = self.GeneratePreferred(state, history, actions, status)
if actions:
for action in actions:
qnode = vnode.Child(action)
qnode.Value.Set(self.Knowledge.SmartTreeCount,
self.Knowledge.SmartTreeValue)
qnode.AMAF.Set(self.Knowledge.SmartTreeCount,
self.Knowledge.SmartTreeValue)
vnode.Children[action] = qnode
return vnode
#---- Displays
def DisplayBeliefs(self, beliefState):
return
def DisplayState(self, state):
return
def DisplayAction(self, state, action):
print("Action ", action)
def DisplayObservation(self, state, observation):
print("Observation ", observation)
def DisplayReward(self, reward):
print("Reward ", reward)
#--- Accessors
def SetKnowledge(self, knowledge):
self.Knowledge = knowledge
def GetNumActions(self):
return self.NumActions
def GetNumObservations(self):
return self.NumObservations
def GetDiscount(self):
return self.Discount
def GetHyperbolicDiscount(self, t, kappa=0.277, sigma=1.0):
return 1.0 / np.power((1.0 + kappa * float(t)), sigma)
def GetRewardRange(self):
return self.RewardRange
def GetHorizon(self, accuracy, undiscountedHorizon):
if self.Discount == 1:
return undiscountedHorizon
return np.log(accuracy) / np.log(self.Discount)
def GetPath(occlusions):
ASTAR = AStar(XSize, YSize, occlusions)
ASTAR.InitializeGrid(AgentHome, GoalPos)
path = ASTAR.Solve()
return path