def Start(self):
filePath = self.Config.FilePath.BestModel
cnf = "BestLog/BestLog180126082522.cnf"
wgt = "BestLog/BestLog180126082522.wgt"
timeLimit = 0.9
net = Network()
net.Load(cnf, wgt)
net.TimeLimit = timeLimit
model = Model()
taskName = "TaskEval/EvalTask114.task"
task = MujocoTask(model, taskName)
#task = MujocoTask.LoadRandom(model, self.Config.Task.EvalDir)
env = MujocoEnv(model)
agentConfig = self.Config.ViewerAgent
agent = Agent(agentConfig, net, model, task)
bestAction = agent.SearchBestAction()
while True:
env.SetSimState(task.StartState)
for action in bestAction:
env.Step(action)
#print(env.GetObservation(task))
env.Render()
def Expand(self, network: NetworkModel, env: MujocoEnv, task, valueCalc):
env.SetSimState(self.Parent.State)
env.Step(self.ActionNum)
self.State = env.GetSimState()
self.Observation = env.GetObservation(task, network.TimeLimit)
self.Score = env.GetScore(task)
self.IsTerminate = env.IsTerminate(task, self.Score, network.TimeLimit)
policy_arr, value_arr = network.Model.predict(
np.array([self.Observation]))
policy = policy_arr[0]
value = np.sum(value_arr[0]) / len(value_arr[0])
if self.IsTerminate == True:
value = valueCalc.CalcValue(self.Score)
for i in range(len(policy)):
self.Children.append(Node(self, policy[i], i))
self.IsExpanded = True
self.N = 1
self.W = value
self.Q = value
return value
def MakeHopperTask(self, modelNum, trainNum, taskTrainDir, evalNum,
taskEvalDir):
env = MujocoEnv(self)
state = env.GetSimState()
jsons = []
for j in range(modelNum):
if j != 0:
state.qpos[env.Model.MujocoModel.get_joint_qpos_addr(
"rootx")] = random.uniform(-0.8, 0.8)
state.qpos[env.Model.MujocoModel.get_joint_qpos_addr(
"rootz")] = random.uniform(2, 2)
state.qpos[env.Model.MujocoModel.get_joint_qpos_addr(
"thigh_joint")] = pt = random.uniform(-0.1, -0.5)
state.qpos[env.Model.MujocoModel.get_joint_qpos_addr(
"leg_joint")] = pl = random.uniform(-0.1, -0.5)
state.qpos[env.Model.MujocoModel.get_joint_qpos_addr(
"rooty")] = random.uniform(-0.2, 0.2) + (pt + pl) / 2
state.qpos[env.Model.MujocoModel.get_joint_qpos_addr(
"foot_joint")] = random.uniform(-0.5, 0.5)
env.SetSimState(state)
ok = False
for i in range(400):
env.Step(env.GetActionNum() - 1)
sensor = env.GetSensorValue(3, "a_foot_joint")
if sensor[2] >= 20 and i >= 10:
ok = True
break
if ok == False:
continue
joints = [
'thigh_joint', 'leg_joint', 'foot_joint', 'rooty', 'rootx',
'rootz'
]
task = {}
for i in joints:
task[i] = env.GetSensorValue(1, "jp_" + i)[0]
jsons.append(task)
for i in range(trainNum):
filePath = taskTrainDir + "/TrainTask" + str(i) + ".task"
print(filePath)
task1 = random.choice(jsons)
task2 = jsons[0]
with open(filePath, "wt") as f:
json.dump(list([task1, task2]), f)
for i in range(evalNum):
filePath = taskEvalDir + "/EvalTask" + str(i) + ".task"
print(filePath)
task1 = random.choice(jsons)
task2 = jsons[0]
with open(filePath, "wt") as f:
json.dump(list([task1, task2]), f)