Пример #1
0
 def __init__(self,learningArgs,motionModelArgs):
     lr = learningArgs[0]
     lrDecay_stepSize = learningArgs[1]
     lrDecay_gamma = learningArgs[2]
     weight_decay = learningArgs[3]
     self.device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
     self.MotionModel = deterministicMotionModel(motionModelArgs).to(self.device)
     self.optimizer = Adam(self.MotionModel.parameters(),lr = lr,weight_decay=weight_decay)
     self.lrScheduler = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=lrDecay_stepSize, gamma=lrDecay_gamma)
     self.criterion = torch.nn.MSELoss()
sim.resetClifford()
startState = sim.controlLoopStep([0, 0])
p.disconnect(physicsClientId=physicsClientId)

#initialize motion model
inStateDim = len(startState[0][0]) + 1
inActionDim = len(startState[0][2])
inMapDim = startState[0][1].shape[1]
outStateDim = len(startState[1][0])
argDim = [inStateDim, inMapDim, inActionDim, outStateDim]
convSizes = [[32, 5], [32, 4], [32, 3]]
fcSizes = [1024, 512, 256]  #,128]
networkSizes = [convSizes, fcSizes]
dropout_ps = [0, 0, 0]
motionModelArgs = [argDim, networkSizes, dropout_ps]
motionModel = deterministicMotionModel(motionModelArgs).to(device)
motionModel.load_state_dict(
    torch.load('../motionModels/v2sequentialDeterministic.pt'))
motionModel.eval()

# initialize all particles
numParticles = 10
particleStates = cliffordStateTransformation(torch.tensor(
    startState[3]).unsqueeze(0).to(device),
                                             numParticles=numParticles)
predX = [[startState[3][0]] for i in range(numParticles)]
predY = [[startState[3][1]] for i in range(numParticles)]

# loop through time and take action
for t in range(20):
    # get current state of particles