def Main():
'''Entrypoint to the program.'''
# PARAMETERS =====================================================================================
params = loadmat("parameters.mat", squeeze_me=True)
randomSeed = params["randomSeed"]
tMax = params["tMax"]
nEpisodes = params["nEpisodes"]
trainEvery = params["trainEvery"]
unbiasOnEpisode = params["unbiasOnEpisode"]
saveFileName = params["saveFileName"]
loadNetwork = params["loadNetwork"]
loadDatabase = params["loadDatabase"]
showSteps = params["showSteps"]
# INITIALIZATION =================================================================================
# set random seeds
seed(randomSeed)
tensorflow.random.set_seed(randomSeed)
# initialize agent and environment
rlEnv = RlEnvironmentBottlesOnCoasters(params)
rlAgent = RlAgent(params)
# if testing, load previous results
if loadNetwork:
rlAgent.LoadQFunction()
if loadDatabase:
rlAgent.LoadExperienceDatabase()
# RUN TEST =======================================================================================
episodeReturn = []
nPlacedObjects = []
nGraspedObjects = []
episodeTime = []
timeStepEpsilon = []
databaseSize = []
losses = []
for episode in xrange(nEpisodes):
startTime = time()
# place random object in random orientation on table
rlEnv.MoveHandToHoldingPose()
rlEnv.PlaceObjects(False)
rlEnv.PlaceObjects(True)
if showSteps: raw_input("Placed objects.")
R = 0
nPlaced = 0
nGrasped = 0
holdingDesc = None
observations = []
actions = []
rewards = []
isGrasp = []
for t in xrange(tMax):
# get a point cloud
cloud = rlEnv.GetArtificialCloud()
#rlEnv.PlotCloud(cloud)
#if showSteps: raw_input("Acquired cloud.")
isGrasp.append(holdingDesc is None)
# get the next action
o, a, overtDesc, epsilon = rlAgent.SenseAndAct(
holdingDesc, cloud, t, rlEnv, episode >= unbiasOnEpisode)
# perform transition
holdingDesc, r = rlEnv.Transition(overtDesc, cloud)
# save experiences
timeStepEpsilon.append(epsilon)
observations.append(o)
actions.append(a)
rewards.append(r)
R += r
# compute task success -- number of objects placed
if isGrasp[-1]:
if holdingDesc is not None:
nGrasped += 1
if r < 0:
nPlaced -= 1
else:
if r == 1:
nPlaced += 1
rlAgent.AddExperienceMonteCarlo(observations, actions, rewards,
isGrasp)
# cleanup episode
rlEnv.ResetEpisode()
print("Episode {} had return {}".format(episode, R))
# training
if episode % trainEvery == trainEvery - 1:
losses.append(rlAgent.UpdateQFunctionMonteCarlo())
rlAgent.SaveQFunction()
# save results
episodeReturn.append(R)
nPlacedObjects.append(nPlaced)
nGraspedObjects.append(nGrasped)
episodeTime.append(time() - startTime)
databaseSize.append(rlAgent.GetNumberOfExperiences())
if episode % trainEvery == trainEvery - 1 or episode == nEpisodes - 1:
saveData = {
"episodeReturn": episodeReturn,
"nPlacedObjects": nPlacedObjects,
"nGraspedObjects": nGraspedObjects,
"episodeTime": episodeTime,
"timeStepEpsilon": timeStepEpsilon,
"databaseSize": databaseSize,
"losses": losses
}
saveData.update(params)
savemat(saveFileName, saveData)
# backup agent data
if episode == nEpisodes - 1:
rlAgent.SaveExperienceDatabase()
def Main():
'''Entrypoint to the program.'''
# PARAMETERS =====================================================================================
params = loadmat("parameters.mat", squeeze_me=True)
randomSeed = params["randomSeed"]
tMax = params["tMax"]
nEpisodes = params["nEpisodes"]
trainEvery = params["trainEvery"]
unbiasOnEpisode = params["unbiasOnEpisode"]
nObjects = params["nObjects"]
nSurfaceObjects = params["nSurfaceObjects"]
objHeight = params["objHeight"]
objRadius = params["objRadius"]
surfObjHeight = params["surfObjHeight"]
surfObjRadius = params["surfObjRadius"]
saveFileName = params["saveFileName"]
loadNetwork = params["loadNetwork"]
loadDatabase = params["loadDatabase"]
showSteps = params["showSteps"]
# INITIALIZATION =================================================================================
# set random seeds
seed(randomSeed)
tensorflow.random.set_seed(randomSeed)
# initialize agent and environment
rlEnv = RlEnvironmentPegsOnDisks(params)
rlAgent = RlAgent(params)
# if testing, load previous results
if loadNetwork:
rlAgent.LoadQFunction()
if loadDatabase:
rlAgent.LoadExperienceDatabase()
# RUN TEST =======================================================================================
episodeReturn = []
episodeTime = []
timeStepEpsilon = []
databaseSize = []
losses = []
for episode in xrange(nEpisodes):
startTime = time()
# place random object in random orientation on table
rlEnv.MoveHandToHoldingPose()
rlEnv.PlaceCylinders(nObjects, objHeight, objRadius, False)
rlEnv.PlaceCylinders(nSurfaceObjects, surfObjHeight, surfObjRadius,
True)
if showSteps: raw_input("Placed objects.")
R = 0
holdingDesc = None
observations = []
actions = []
rewards = []
for t in xrange(tMax):
# get the next action
o, a, overtDesc, epsilon = rlAgent.SenseAndAct(
holdingDesc, t, rlEnv, episode >= unbiasOnEpisode)
# perform transition
holdingDesc, r = rlEnv.Transition(overtDesc)
# save experiences
timeStepEpsilon.append(epsilon)
observations.append(o)
actions.append(a)
rewards.append(r)
R += r
rlAgent.AddExperienceMonteCarlo(observations, actions, rewards)
# cleanup episode
rlEnv.ResetEpisode()
print("Episode {} had return {}".format(episode, R))
# training
if episode % trainEvery == trainEvery - 1:
losses.append(rlAgent.UpdateQFunctionMonteCarlo())
rlAgent.SaveQFunction()
# save results
episodeReturn.append(R)
episodeTime.append(time() - startTime)
databaseSize.append(rlAgent.GetNumberOfExperiences())
if episode % trainEvery == trainEvery - 1 or episode == nEpisodes - 1:
saveData = {
"episodeReturn": episodeReturn,
"episodeTime": episodeTime,
"timeStepEpsilon": timeStepEpsilon,
"databaseSize": databaseSize,
"losses": losses
}
saveData.update(params)
savemat(saveFileName, saveData)
# backup agent data
if episode == nEpisodes - 1:
rlAgent.SaveExperienceDatabase()