def step(self, packet, weights, backward):
'''Synchronizes weights from upstream; computes
agent decisions; computes policy updates.
Args:
packet : An IO object specifying observations
weights : An optional parameter vector to replace model weights
backward : (bool) Whether of not a backward pass should be performed
Returns:
data : The same IO object populated with action decisions
grads : A vector of gradients aggregated across trajectories
summary : A BlobSummary object logging agent statistics
'''
grads, blobs = None, None
#Sync model weights; batch obs; compute forward pass
setParameters(self.net, weights)
self.manager.collectInputs(packet)
self.net(packet, self.manager)
#Compute backward pass and logs from full rollouts,
#discarding any partial trajectories
if backward and not self.config.TEST:
rollouts, blobs = self.manager.step()
optim.backward(rollouts, self.config)
#self.manager.inputs.clear()
grads = self.net.grads()
return packet, grads, blobs
def recvUpdate(self, update):
update, update_lm = update
for idx, paramVec in enumerate(update):
setParameters(self.anns[idx], paramVec)
zeroGrads(self.anns[idx])
setParameters(self.lawmaker, update_lm[0])
zeroGrads(self.lawmaker)
def permuteNet(self, goodIdx, badIdx):
goodNet = self.model.net.net[goodIdx]
badNet = self.model.net.net[badIdx]
goodParams = getParameters(goodNet)
noise = self.config.PERMVAL * np.random.randn(len(goodParams))
goodParams = np.array(goodParams) + noise
setParameters(badNet, goodParams)
def recvUpdate(self, update):
if update is None:
return
setParameters(self, update)
def recvUpdate(self, update):
for idx, paramVec in enumerate(update):
setParameters(self.anns[idx], paramVec)
zeroGrads(self.anns[idx])
def syncParameters(self):
parameters = self.parameters.detach().numpy().tolist()
setParameters(self.net, parameters)
