def sendData(settings):
from util.SimulationUtil import getDataDirectory, getAgentName
from_email = "*****@*****.**"
email_subject = "Simulation Data"
email_text = """
This email includes some data on the current state of the simulation. \n
Take care.\n
"""
directory = getDataDirectory(settings)
agentName = getAgentName(settings)
print("Data folder: ", directory)
trainingGraph = directory + agentName + '_' + ".png"
try:
send_mail(send_from=from_email,
send_to=['*****@*****.**'],
subject=email_subject,
text=email_text,
files=[trainingGraph])
except Exception as e:
print 'Error email data: %s' % e
except:
print "Emailling of simulation data failed "
print "Unexpected error:", sys.exc_info()[0]
pass
def combineNetworkModels(settings_file_name):
from model.ModelUtil import getSettings
settings = getSettings(settings_file_name)
# settings['shouldRender'] = True
import os
os.environ['THEANO_FLAGS'] = "mode=FAST_RUN,device=" + settings[
'training_processor_type'] + ",floatX=" + settings['float_type']
## Theano needs to be imported after the flags are set.
# from ModelEvaluation import *
# from model.ModelUtil import *
from ModelEvaluation import SimWorker, evalModelParrallel, collectExperience
# from model.ModelUtil import validBounds
from model.LearningAgent import LearningAgent, LearningWorker
from util.SimulationUtil import validateSettings, createEnvironment, createRLAgent, createActor
from util.SimulationUtil import getDataDirectory, createForwardDynamicsModel, createSampler, getAgentName
from util.ExperienceMemory import ExperienceMemory
from RLVisualize import RLVisualize
from NNVisualize import NNVisualize
directory = getDataDirectory(settings)
rounds = settings["rounds"]
epochs = settings["epochs"]
# num_states=settings["num_states"]
epsilon = settings["epsilon"]
discount_factor = settings["discount_factor"]
reward_bounds = np.array(settings["reward_bounds"])
# reward_bounds = np.array([[-10.1],[0.0]])
batch_size = settings["batch_size"]
train_on_validation_set = settings["train_on_validation_set"]
state_bounds = np.array(settings['state_bounds'])
discrete_actions = np.array(settings['discrete_actions'])
num_actions = discrete_actions.shape[0] # number of rows
print("Sim config file name: " + str(settings["sim_config_file"]))
# c = characterSim.Configuration(str(settings["sim_config_file"]))
# c = characterSim.Configuration("../data/epsilon0Config.ini")
action_space_continuous = settings['action_space_continuous']
settings['load_saved_model'] = False
new_model = createRLAgent(settings['agent_name'], state_bounds,
discrete_actions, reward_bounds, settings)
params = new_model.getNetworkParameters()
print("New Network Critic shape")
for i in range(len(params[0])):
print(params[0][i].shape)
print("New Network Critic shape, done")
if (True):
file_name = directory + getAgentName() + ".pkl"
else:
file_name = directory + getAgentName() + "_Best.pkl"
print("Loading model: ", file_name)
f = open(file_name, 'rb')
old_model = dill.load(f)
f.close()
print("State Length: ", len(old_model.getStateBounds()[0]))
if (True):
new_model.setAgentNetworkParamters(old_model)
new_model.setCombinedNetworkParamters(old_model)
# new_model.setMergeLayerNetworkParamters(old_model)
new_model.setMergeLayerNetworkParamters(old_model,
zeroInjectedMergeLayer=True)
else:
new_model.setNetworkParameters(old_model.getNetworkParameters())
params = new_model.getNetworkParameters()
print("New Network Critic shape")
for i in range(len(params[0])):
print(params[0][i].shape)
for i in range(len(params[1])):
print(params[1][i].shape)
### Modify state bounds
state_bounds[:, settings['num_terrain_features']:len(
state_bounds[0])] = old_model.getStateBounds()
print("State bounds: ", state_bounds.shape)
print(state_bounds)
new_model.setStateBounds(state_bounds)
new_model.setActionBounds(old_model.getActionBounds())
new_model.setRewardBounds(old_model.getRewardBounds())
file_name = directory + getAgentName() + "_Injected.pkl"
f = open(file_name, 'wb')
dill.dump(new_model, f)
f.close()
def trainModelParallel(inputData):
settingsFileName = inputData[0]
settings = inputData[1]
np.random.seed(int(settings['random_seed']))
import os
if ('THEANO_FLAGS' in os.environ):
os.environ['THEANO_FLAGS'] = os.environ[
'THEANO_FLAGS'] + "mode=FAST_RUN,device=" + settings[
'training_processor_type'] + ",floatX=" + settings['float_type']
else:
os.environ['THEANO_FLAGS'] = "mode=FAST_RUN,device=" + settings[
'training_processor_type'] + ",floatX=" + settings['float_type']
import keras.backend
keras.backend.set_floatx(settings['float_type'])
print("K.floatx()", keras.backend.floatx())
from ModelEvaluation import SimWorker, evalModelParrallel, collectExperience, simEpoch, evalModel, simModelParrallel
from model.ModelUtil import validBounds, fixBounds, anneal_value
from model.LearningAgent import LearningAgent, LearningWorker
from util.SimulationUtil import validateSettings
from util.SimulationUtil import createEnvironment
from util.SimulationUtil import createRLAgent
from util.SimulationUtil import createActor, getAgentName
from util.SimulationUtil import getDataDirectory, createForwardDynamicsModel, createSampler
from util.ExperienceMemory import ExperienceMemory
from RLVisualize import RLVisualize
from NNVisualize import NNVisualize
#from sim.PendulumEnvState import PendulumEnvState
#from sim.PendulumEnv import PendulumEnv
#from sim.BallGame2DEnv import BallGame2DEnv
settings = validateSettings(settings)
model_type = settings["model_type"]
directory = getDataDirectory(settings)
if not os.path.exists(directory):
os.makedirs(directory)
# copy settings file
out_file_name = directory + os.path.basename(settingsFileName)
print("Saving settings file with data: ", out_file_name)
out_file = open(out_file_name, 'w')
out_file.write(json.dumps(settings, indent=4))
out_file.close()
### Try and save algorithm and model files for reference
if "." in settings['model_type']:
### convert . to / and copy file over
file_name = settings['model_type']
k = file_name.rfind(".")
file_name = file_name[:k]
file_name_read = file_name.replace(".", "/")
file_name_read = file_name_read + ".py"
print("model file name:", file_name)
print("os.path.basename(file_name): ", os.path.basename(file_name))
file = open(file_name_read, 'r')
out_file = open(directory + file_name + ".py", 'w')
out_file.write(file.read())
file.close()
out_file.close()
if "." in settings['agent_name']:
### convert . to / and copy file over
file_name = settings['agent_name']
k = file_name.rfind(".")
file_name = file_name[:k]
file_name_read = file_name.replace(".", "/")
file_name_read = file_name_read + ".py"
print("model file name:", file_name)
print("os.path.basename(file_name): ", os.path.basename(file_name))
file = open(file_name_read, 'r')
out_file = open(directory + file_name + ".py", 'w')
out_file.write(file.read())
file.close()
out_file.close()
if (settings['train_forward_dynamics']):
if "." in settings['forward_dynamics_model_type']:
### convert . to / and copy file over
file_name = settings['forward_dynamics_model_type']
k = file_name.rfind(".")
file_name = file_name[:k]
file_name_read = file_name.replace(".", "/")
file_name_read = file_name_read + ".py"
print("model file name:", file_name)
print("os.path.basename(file_name): ", os.path.basename(file_name))
file = open(file_name_read, 'r')
out_file = open(directory + file_name + ".py", 'w')
out_file.write(file.read())
file.close()
out_file.close()
rounds = settings["rounds"]
epochs = settings["epochs"]
epsilon = settings["epsilon"]
discount_factor = settings["discount_factor"]
reward_bounds = np.array(settings["reward_bounds"])
batch_size = settings["batch_size"]
train_on_validation_set = settings["train_on_validation_set"]
state_bounds = np.array(settings['state_bounds'])
discrete_actions = np.array(settings['discrete_actions']) #9*6
num_actions = discrete_actions.shape[0] # number of rows
print("Sim config file name: " + str(settings["sim_config_file"]))
action_space_continuous = settings['action_space_continuous']
if (settings['num_available_threads'] == 1):
input_anchor_queue = multiprocessing.Queue(
settings['queue_size_limit'])
input_anchor_queue_eval = multiprocessing.Queue(
settings['queue_size_limit'])
output_experience_queue = multiprocessing.Queue(
settings['queue_size_limit'])
eval_episode_data_queue = multiprocessing.Queue(
settings['queue_size_limit'])
else:
input_anchor_queue = multiprocessing.Queue(settings['epochs'])
input_anchor_queue_eval = multiprocessing.Queue(settings['epochs'])
output_experience_queue = multiprocessing.Queue(
settings['queue_size_limit'])
eval_episode_data_queue = multiprocessing.Queue(
settings['eval_epochs'])
if (settings['on_policy']): ## So that off policy agent does not learn
output_experience_queue = None
sim_work_queues = []
action_space_continuous = settings['action_space_continuous']
if action_space_continuous:
action_bounds = np.array(settings["action_bounds"], dtype=float)
### Using a wrapper for the type of actor now
actor = createActor(settings['environment_type'], settings, None)
exp_val = None
if (not validBounds(action_bounds)):
# Check that the action bounds are spcified correctly
print("Action bounds invalid: ", action_bounds)
sys.exit()
if (not validBounds(state_bounds)):
# Probably did not collect enough bootstrapping samples to get good state bounds.
print("State bounds invalid: ", state_bounds)
state_bounds = fixBounds(np.array(state_bounds))
bound_fixed = validBounds(state_bounds)
print("State bounds fixed: ", bound_fixed)
sys.exit()
if (not validBounds(reward_bounds)):
print("Reward bounds invalid: ", reward_bounds)
sys.exit()
if settings['action_space_continuous']:
experience = ExperienceMemory(len(state_bounds[0]),
len(action_bounds[0]),
settings['expereince_length'],
continuous_actions=True,
settings=settings)
else:
experience = ExperienceMemory(len(state_bounds[0]), 1,
settings['expereince_length'])
experience.setSettings(settings)
if settings['visualize_learning']:
title = settings['agent_name']
k = title.rfind(".") + 1
if (k > len(title)): ## name does not contain a .
k = 0
title = title[k:]
rlv = RLVisualize(title=title + " agent on " +
str(settings['environment_type']),
settings=settings)
rlv.setInteractive()
rlv.init()
if (settings['train_forward_dynamics']):
if settings['visualize_learning']:
title = settings['forward_dynamics_model_type']
k = title.rfind(".") + 1
if (k > len(title)): ## name does not contain a .
k = 0
title = title[k:]
nlv = NNVisualize(title=str("Dynamics Model") + " with " + title,
settings=settings)
nlv.setInteractive()
nlv.init()
if (settings['train_reward_predictor']):
if settings['visualize_learning']:
title = settings['forward_dynamics_model_type']
k = title.rfind(".") + 1
if (k > len(title)): ## name does not contain a .
k = 0
title = title[k:]
rewardlv = NNVisualize(title=str("Reward Model") + " with " +
title,
settings=settings)
rewardlv.setInteractive()
rewardlv.init()
if (settings['debug_critic']): #True
criticLosses = []
criticRegularizationCosts = []
if (settings['visualize_learning']):
title = settings['agent_name']
k = title.rfind(".") + 1
if (k > len(title)): ## name does not contain a .
k = 0
title = title[k:]
critic_loss_viz = NNVisualize(title=str("Critic Loss") + " with " +
title)
critic_loss_viz.setInteractive()
critic_loss_viz.init()
critic_regularization_viz = NNVisualize(
title=str("Critic Reg Cost") + " with " + title)
critic_regularization_viz.setInteractive()
critic_regularization_viz.init()
if (settings['debug_actor']): # True
actorLosses = []
actorRegularizationCosts = []
if (settings['visualize_learning']): #False
title = settings['agent_name']
k = title.rfind(".") + 1
if (k > len(title)): ## name does not contain a .
k = 0
title = title[k:]
actor_loss_viz = NNVisualize(title=str("Actor Loss") + " with " +
title)
actor_loss_viz.setInteractive()
actor_loss_viz.init()
actor_regularization_viz = NNVisualize(
title=str("Actor Reg Cost") + " with " + title)
actor_regularization_viz.setInteractive()
actor_regularization_viz.init()
model = createRLAgent(settings['agent_name'], state_bounds,
discrete_actions, reward_bounds,
settings) #return a model class
forwardDynamicsModel = None
if (settings['train_forward_dynamics']): #False
if (settings['forward_dynamics_model_type'] == "SingleNet"):
print(
"Creating forward dynamics network: Using single network model"
)
forwardDynamicsModel = createForwardDynamicsModel(settings,
state_bounds,
action_bounds,
None,
None,
agentModel=model)
else:
print("Creating forward dynamics network")
forwardDynamicsModel = createForwardDynamicsModel(settings,
state_bounds,
action_bounds,
None,
None,
agentModel=None)
forwardDynamicsModel.setActor(actor)
forwardDynamicsModel.init(len(state_bounds[0]), len(action_bounds[0]),
state_bounds, action_bounds, actor, None,
settings)
(agent,
learning_workers) = createLearningAgent(settings, output_experience_queue,
state_bounds, action_bounds,
reward_bounds)
masterAgent = agent
### These are the workers for training
(sim_workers, sim_work_queues) = createSimWorkers(
settings, input_anchor_queue, output_experience_queue,
eval_episode_data_queue, model, forwardDynamicsModel, exp_val,
state_bounds, action_bounds, reward_bounds)
eval_sim_workers = sim_workers
eval_sim_work_queues = sim_work_queues
if ('override_sim_env_id' in settings
and (settings['override_sim_env_id'] != False)): #True
(eval_sim_workers, eval_sim_work_queues) = createSimWorkers(
settings,
input_anchor_queue_eval,
output_experience_queue,
eval_episode_data_queue,
model,
forwardDynamicsModel,
exp_val,
state_bounds,
action_bounds,
reward_bounds,
default_sim_id=settings['override_sim_env_id']) # id=1
else:
input_anchor_queue_eval = input_anchor_queue
best_eval = -100000000.0
best_dynamicsLosses = best_eval * -1.0
values = []
discounted_values = []
bellman_error = []
reward_over_epoc = []
dynamicsLosses = []
dynamicsRewardLosses = []
for lw in learning_workers:
print("Learning worker")
print(lw)
if (int(settings["num_available_threads"]) > 1):
for sw in sim_workers:
print("Sim worker")
print(sw)
sw.start()
if ('override_sim_env_id' in settings
and (settings['override_sim_env_id'] != False)):
for sw in eval_sim_workers:
print("Sim worker")
print(sw)
sw.start()
## This needs to be done after the simulation worker processes are created
exp_val = createEnvironment(settings["forwardDynamics_config_file"],
settings['environment_type'],
settings,
render=settings['shouldRender'],
index=0)
exp_val.setActor(actor)
exp_val.getActor().init()
exp_val.init()
### This is for a single-threaded Synchronous sim only.
if (int(settings["num_available_threads"]) == 1
): # This is okay if there is one thread only...
sim_workers[0].setEnvironment(exp_val)
sim_workers[0].start()
if ('override_sim_env_id' in settings
and (settings['override_sim_env_id'] != False)):
eval_sim_workers[0].setEnvironment(exp_val)
eval_sim_workers[0].start()
masterAgent.setPolicy(model)
if (settings['train_forward_dynamics']):
masterAgent.setForwardDynamics(forwardDynamicsModel)
tmp_p = 1.0
message = {}
if (settings['load_saved_model']):
tmp_p = settings['min_epsilon']
data = ('Update_Policy', tmp_p, model.getStateBounds(),
model.getActionBounds(), model.getRewardBounds(),
masterAgent.getPolicy().getNetworkParameters())
if (settings['train_forward_dynamics']):
data = ('Update_Policy', tmp_p, model.getStateBounds(),
model.getActionBounds(), model.getRewardBounds(),
masterAgent.getPolicy().getNetworkParameters(),
masterAgent.getForwardDynamics().getNetworkParameters())
message['type'] = 'Update_Policy'
message['data'] = data
for m_q in sim_work_queues:
print("trainModel: Sending current network parameters: ", m_q)
m_q.put(message)
if (int(settings["num_available_threads"]) == 1):
experience, state_bounds, reward_bounds, action_bounds = collectExperience(
actor,
exp_val,
model,
settings,
sim_work_queues=None,
eval_episode_data_queue=None
) #experience: state, action, nextstate, rewards,
else:
if (settings['on_policy']):
experience, state_bounds, reward_bounds, action_bounds = collectExperience(
actor,
None,
model,
settings,
sim_work_queues=sim_work_queues,
eval_episode_data_queue=eval_episode_data_queue)
else:
experience, state_bounds, reward_bounds, action_bounds = collectExperience(
actor,
None,
model,
settings,
sim_work_queues=input_anchor_queue,
eval_episode_data_queue=eval_episode_data_queue)
masterAgent.setExperience(experience)
if ('keep_seperate_fd_exp_buffer' in settings
and (settings['keep_seperate_fd_exp_buffer'])):
masterAgent.setFDExperience(copy.deepcopy(experience))
if (not validBounds(action_bounds)):
# Check that the action bounds are spcified correctly
print("Action bounds invalid: ", action_bounds)
sys.exit()
if (not validBounds(state_bounds)):
# Probably did not collect enough bootstrapping samples to get good state bounds.
print("State bounds invalid: ", state_bounds)
state_bounds = fixBounds(np.array(state_bounds))
bound_fixed = validBounds(state_bounds)
print("State bounds fixed: ", bound_fixed)
if (not validBounds(reward_bounds)):
print("Reward bounds invalid: ", reward_bounds)
sys.exit()
print("Reward History: ", experience._reward_history)
print("Action History: ", experience._action_history)
print("Action Mean: ", np.mean(experience._action_history))
print("Experience Samples: ", (experience.samples()))
if (settings["save_experience_memory"]):
print("Saving initial experience memory")
file_name = directory + getAgentName() + "_expBufferInit.hdf5"
experience.saveToFile(file_name)
if (settings['load_saved_model']
or (settings['load_saved_model']
== 'network_and_scales')): ## Transfer learning
experience.setStateBounds(copy.deepcopy(model.getStateBounds()))
experience.setRewardBounds(copy.deepcopy(model.getRewardBounds()))
experience.setActionBounds(copy.deepcopy(model.getActionBounds()))
model.setSettings(settings)
else: ## Normal
model.setStateBounds(state_bounds)
model.setActionBounds(action_bounds)
model.setRewardBounds(reward_bounds)
experience.setStateBounds(copy.deepcopy(model.getStateBounds()))
experience.setRewardBounds(copy.deepcopy(model.getRewardBounds()))
experience.setActionBounds(copy.deepcopy(model.getActionBounds()))
masterAgent_message_queue = multiprocessing.Queue(settings['epochs'])
if (settings['train_forward_dynamics']):
if (not settings['load_saved_model']):
forwardDynamicsModel.setStateBounds(state_bounds)
forwardDynamicsModel.setActionBounds(action_bounds)
forwardDynamicsModel.setRewardBounds(reward_bounds)
masterAgent.setForwardDynamics(forwardDynamicsModel)
## Now everything related to the exp memory needs to be updated
bellman_errors = []
masterAgent.setPolicy(model)
print("Master agent state bounds: ",
repr(masterAgent.getPolicy().getStateBounds()))
for sw in sim_workers: # Need to update parameter bounds for models
print("exp: ", sw._exp)
print("sw modle: ", sw._model.getPolicy())
## If not on policy
if (not settings['on_policy']):
for lw in learning_workers:
lw._agent.setPolicy(model)
lw.setMasterAgentMessageQueue(masterAgent_message_queue)
lw.updateExperience(experience)
print("ls policy: ", lw._agent.getPolicy())
lw.start()
tmp_p = 1.0
if (settings['load_saved_model']):
tmp_p = settings['min_epsilon']
data = ('Update_Policy', tmp_p, model.getStateBounds(),
model.getActionBounds(), model.getRewardBounds(),
masterAgent.getPolicy().getNetworkParameters())
if (settings['train_forward_dynamics']):
data = ('Update_Policy', tmp_p, model.getStateBounds(),
model.getActionBounds(), model.getRewardBounds(),
masterAgent.getPolicy().getNetworkParameters(),
masterAgent.getForwardDynamics().getNetworkParameters())
message['type'] = 'Update_Policy'
message['data'] = data
for m_q in sim_work_queues:
print("trainModel: Sending current network parameters: ", m_q)
m_q.put(message)
del model
## Give gloabl access to processes to they can be terminated when ctrl+c is pressed
global sim_processes
sim_processes = sim_workers
global learning_processes
learning_processes = learning_workers
global _input_anchor_queue
_input_anchor_queue = input_anchor_queue
global _output_experience_queue
_output_experience_queue = output_experience_queue
global _eval_episode_data_queue
_eval_episode_data_queue = eval_episode_data_queue
global _sim_work_queues
_sim_work_queues = sim_work_queues
trainData = {}
trainData["mean_reward"] = []
trainData["std_reward"] = []
trainData["mean_bellman_error"] = []
trainData["std_bellman_error"] = []
trainData["mean_discount_error"] = []
trainData["std_discount_error"] = []
trainData["mean_forward_dynamics_loss"] = []
trainData["std_forward_dynamics_loss"] = []
trainData["mean_forward_dynamics_reward_loss"] = []
trainData["std_forward_dynamics_reward_loss"] = []
trainData["mean_eval"] = []
trainData["std_eval"] = []
trainData["mean_critic_loss"] = []
trainData["std_critic_loss"] = []
trainData["mean_critic_regularization_cost"] = []
trainData["std_critic_regularization_cost"] = []
trainData["mean_actor_loss"] = []
trainData["std_actor_loss"] = []
trainData["mean_actor_regularization_cost"] = []
trainData["std_actor_regularization_cost"] = []
trainData["anneal_p"] = []
if (False):
print("State Bounds:", masterAgent.getStateBounds())
print("Action Bounds:", masterAgent.getActionBounds())
print("Exp State Bounds: ", experience.getStateBounds())
print("Exp Action Bounds: ", experience.getActionBounds())
print("Starting first round")
if (settings['on_policy']):
sim_epochs_ = epochs
for round_ in range(
0, rounds): #annel value # the parameter of greedy exploration
if ('annealing_schedule' in settings
and (settings['annealing_schedule'] != False)):
p = anneal_value(float(round_ / rounds), settings_=settings)
else:
p = ((settings['initial_temperature'] / math.log(round_ + 2)))
p = max(settings['min_epsilon'],
min(settings['epsilon'], p)) # Keeps it between 1.0 and 0.2
if (settings['load_saved_model']):
p = settings['min_epsilon']
for epoch in range(epochs):
if (settings['on_policy']):
out = simModelParrallel(
sw_message_queues=sim_work_queues,
model=masterAgent,
settings=settings,
eval_episode_data_queue=eval_episode_data_queue,
anchors=settings['num_on_policy_rollouts'])
(
tuples, discounted_sum, q_value, evalData
) = out # tuples = states, actions, result_states, rewards, falls, G_ts, advantage, exp_actions
(__states, __actions, __result_states, __rewards, __falls,
__G_ts, advantage__, exp_actions__) = tuples
for i in range(1):
masterAgent.train(_states=__states,
_actions=__actions,
_rewards=__rewards,
_result_states=__result_states,
_falls=__falls,
_advantage=advantage__,
_exp_actions=exp_actions__)
if (('anneal_on_policy' in settings)
and settings['anneal_on_policy']):
p_tmp_ = p
else:
p_tmp_ = 1.0
data = ('Update_Policy', p_tmp_, masterAgent.getStateBounds(),
masterAgent.getActionBounds(),
masterAgent.getRewardBounds(),
masterAgent.getPolicy().getNetworkParameters())
message = {}
message['type'] = 'Update_Policy'
message['data'] = data
if (settings['train_forward_dynamics']):
data = ('Update_Policy', p_tmp_,
masterAgent.getStateBounds(),
masterAgent.getActionBounds(),
masterAgent.getRewardBounds(),
masterAgent.getPolicy().getNetworkParameters(),
masterAgent.getForwardDynamics().
getNetworkParameters())
message['data'] = data
for m_q in sim_work_queues:
## block on full queue
m_q.put(message)
if ('override_sim_env_id' in settings
and (settings['override_sim_env_id'] != False)):
for m_q in eval_sim_work_queues:
## block on full queue
m_q.put(message)
else:
episodeData = {}
episodeData['data'] = epoch
episodeData['type'] = 'sim'
input_anchor_queue.put(episodeData)
if masterAgent.getExperience().samples(
) >= batch_size: #更新policy网络
states, actions, result_states, rewards, falls, G_ts, exp_actions = masterAgent.getExperience(
).get_batch(batch_size)
error = masterAgent.bellman_error(states, actions, rewards,
result_states, falls)
bellman_errors.append(error)
if (settings['debug_critic']):
loss__ = masterAgent.getPolicy()._get_critic_loss(
) # uses previous call batch data
criticLosses.append(loss__)
regularizationCost__ = masterAgent.getPolicy(
)._get_critic_regularization()
criticRegularizationCosts.append(regularizationCost__)
if (settings['debug_actor']): #True
loss__ = masterAgent.getPolicy()._get_actor_loss(
) # uses previous call batch data
actorLosses.append(loss__)
regularizationCost__ = masterAgent.getPolicy(
)._get_actor_regularization()
actorRegularizationCosts.append(regularizationCost__)
if not all(np.isfinite(error)):
print(
"States: " + str(states) + " ResultsStates: " +
str(result_states) + " Rewards: " + str(rewards) +
" Actions: " + str(actions) + " Falls: ", str(falls))
print("Bellman Error is Nan: " + str(error) +
str(np.isfinite(error)))
sys.exit()
error = np.mean(np.fabs(error))
if error > 10000:
print("Error to big: ")
print(states, actions, rewards, result_states)
if (settings['train_forward_dynamics']): #False
dynamicsLoss = masterAgent.getForwardDynamics(
).bellman_error(states, actions, result_states, rewards)
dynamicsLoss = np.mean(np.fabs(dynamicsLoss)) #fabs：计算绝对值
dynamicsLosses.append(dynamicsLoss)
if (settings['train_reward_predictor']):
dynamicsRewardLoss = masterAgent.getForwardDynamics(
).reward_error(states, actions, result_states, rewards)
dynamicsRewardLoss = np.mean(
np.fabs(dynamicsRewardLoss))
dynamicsRewardLosses.append(dynamicsRewardLoss)
if (settings['train_forward_dynamics']):
print("Round: " + str(round_) + " Epoch: " + str(epoch) +
" p: " + str(p) + " With mean reward: " +
str(np.mean(rewards)) + " bellman error: " +
str(error) + " ForwardPredictionLoss: " +
str(dynamicsLoss))
else:
print("Round: " + str(round_) + " Epoch: " + str(epoch) +
" p: " + str(p) + " With mean reward: " +
str(np.mean(rewards)) + " bellman error: " +
str(error))
if (settings["print_levels"][settings["print_level"]] >=
settings["print_levels"]['train']):
print("Master agent experience size: " +
str(masterAgent.getExperience().samples()))
if (not settings['on_policy']):
## There could be stale policy parameters in here, use the last set put in the queue
data = None
while (not masterAgent_message_queue.empty()):
## Don't block
try:
data = masterAgent_message_queue.get(False)
except Exception as inst:
pass
if (not (data == None)):
masterAgent.setExperience(data[0])
masterAgent.getPolicy().setNetworkParameters(data[1])
masterAgent.setStateBounds(
masterAgent.getExperience().getStateBounds())
masterAgent.setActionBounds(
masterAgent.getExperience().getActionBounds())
masterAgent.setRewardBounds(
masterAgent.getExperience().getRewardBounds())
if (settings['train_forward_dynamics']):
masterAgent.getForwardDynamics().setNetworkParameters(
data[2])
if ('keep_seperate_fd_exp_buffer' in settings
and (settings['keep_seperate_fd_exp_buffer'])):
masterAgent.setFDExperience(data[3])
# this->_actor->iterate();
## This will let me know which part of learning is going slower training updates or simulation
if (settings["print_levels"][settings["print_level"]] >=
settings["print_levels"]['train']):
print("sim queue size: ", input_anchor_queue.qsize()) #返回队列的大小
if (output_experience_queue != None):
print("exp tuple queue size: ", output_experience_queue.qsize())
if (not settings['on_policy']):
data = ('Update_Policy', p, masterAgent.getStateBounds(),
masterAgent.getActionBounds(),
masterAgent.getRewardBounds(),
masterAgent.getPolicy().getNetworkParameters())
if (settings['train_forward_dynamics']):
data = (
'Update_Policy', p, masterAgent.getStateBounds(),
masterAgent.getActionBounds(),
masterAgent.getRewardBounds(),
masterAgent.getPolicy().getNetworkParameters(),
masterAgent.getForwardDynamics().getNetworkParameters())
message['type'] = 'Update_Policy'
message['data'] = data
for m_q in sim_work_queues:
## Don't block on full queue
try:
m_q.put(message, False)
except:
print("SimWorker model parameter message queue full: ",
m_q.qsize())
if ('override_sim_env_id' in settings
and (settings['override_sim_env_id'] != False)):
for m_q in eval_sim_work_queues:
## Don't block on full queue
try:
m_q.put(message, False)
except:
print("SimWorker model parameter message queue full: ",
m_q.qsize())
if (round_ % settings['plotting_update_freq_num_rounds']) == 0:
# Running less often helps speed learning up.
# Sync up sim actors
if (settings['on_policy']):
mean_reward, std_reward, mean_bellman_error, std_bellman_error, mean_discount_error, std_discount_error, mean_eval, std_eval = evalModelParrallel(
input_anchor_queue=eval_sim_work_queues,
model=masterAgent,
settings=settings,
eval_episode_data_queue=eval_episode_data_queue,
anchors=settings['eval_epochs'])
else:
mean_reward, std_reward, mean_bellman_error, std_bellman_error, mean_discount_error, std_discount_error, mean_eval, std_eval = evalModelParrallel(
input_anchor_queue=input_anchor_queue_eval,
model=masterAgent,
settings=settings,
eval_episode_data_queue=eval_episode_data_queue,
anchors=settings['eval_epochs'])
print(mean_reward, std_reward, mean_bellman_error,
std_bellman_error, mean_discount_error, std_discount_error)
if mean_bellman_error > 10000:
print("Error to big: ")
else:
if (settings['train_forward_dynamics']): #false
mean_dynamicsLosses = np.mean(dynamicsLosses)
std_dynamicsLosses = np.std(dynamicsLosses)
dynamicsLosses = []
if (settings['train_reward_predictor']): #false
mean_dynamicsRewardLosses = np.mean(dynamicsRewardLosses)
std_dynamicsRewardLosses = np.std(dynamicsRewardLosses)
dynamicsRewardLosses = []
trainData["mean_reward"].append(mean_reward)
trainData["std_reward"].append(std_reward)
trainData["anneal_p"].append(p)
trainData["mean_bellman_error"].append(
np.mean(np.fabs(bellman_errors)))
trainData["std_bellman_error"].append(np.std(bellman_errors))
bellman_errors = []
trainData["mean_discount_error"].append(mean_discount_error)
trainData["std_discount_error"].append(std_discount_error)
trainData["mean_eval"].append(mean_eval)
trainData["std_eval"].append(std_eval)
if (settings['train_forward_dynamics']):
trainData["mean_forward_dynamics_loss"].append(
mean_dynamicsLosses)
trainData["std_forward_dynamics_loss"].append(
std_dynamicsLosses)
if (settings['train_reward_predictor']):
trainData["mean_forward_dynamics_reward_loss"].append(
mean_dynamicsRewardLosses)
trainData["std_forward_dynamics_reward_loss"].append(
std_dynamicsRewardLosses)
if (round_ % settings['saving_update_freq_num_rounds']) == 0:
if (settings['train_forward_dynamics']):
file_name_dynamics = directory + "forward_dynamics_" + ".pkl"
f = open(file_name_dynamics, 'wb')
dill.dump(masterAgent.getForwardDynamics(), f)
f.close()
if mean_dynamicsLosses < best_dynamicsLosses:
best_dynamicsLosses = mean_dynamicsLosses
print("Saving BEST current forward dynamics agent: " +
str(best_dynamicsLosses))
file_name_dynamics = directory + "forward_dynamics_" + "_Best.pkl"
f = open(file_name_dynamics, 'wb')
dill.dump(masterAgent.getForwardDynamics(), f) #save model
f.close()
if (mean_eval > best_eval):
best_eval = mean_eval
print("Saving BEST current agent: " + str(best_eval))
file_name = directory + getAgentName() + "_Best.pkl"
f = open(file_name, 'wb')
dill.dump(masterAgent.getPolicy(), f)
f.close()
if settings['save_trainData']:
fp = open(
directory + "trainingData_" + str(settings['agent_name']) +
".json", 'w')
## because json does not serialize np.float32
for key in trainData:
trainData[key] = [float(i) for i in trainData[key]]
json.dump(trainData, fp)
fp.close()
print("Saving current masterAgent")
file_name = directory + getAgentName() + ".pkl"
f = open(file_name, 'wb')
dill.dump(masterAgent.getPolicy(), f)
f.close()
gc.collect()
print("Terminating Workers")
if (settings['on_policy']):
for m_q in sim_work_queues:
## block on full queue
m_q.put(None)
if ('override_sim_env_id' in settings
and (settings['override_sim_env_id'] != False)):
for m_q in eval_sim_work_queues:
## block on full queue
m_q.put(None)
for sw in sim_workers: # Should update these more often
sw.join()
if ('override_sim_env_id' in settings
and (settings['override_sim_env_id'] != False)):
for sw in eval_sim_workers: # Should update these more often
sw.join()
for i in range(len(sim_work_queues)):
print("sim_work_queues size: ", sim_work_queues[i].qsize())
while (not sim_work_queues[i].empty()): ### Empty the queue
## Don't block
try:
data_ = sim_work_queues[i].get(False)
except Exception as inst:
pass
print("sim_work_queues size: ", sim_work_queues[i].qsize())
for i in range(len(eval_sim_work_queues)):
print("eval_sim_work_queues size: ", eval_sim_work_queues[i].qsize())
while (not eval_sim_work_queues[i].empty()): ### Empty the queue
## Don't block
try:
data_ = eval_sim_work_queues[i].get(False)
except Exception as inst:
pass
print("eval_sim_work_queues size: ", eval_sim_work_queues[i].qsize())
print("Finish sim")
exp_val.finish()
print("Save last versions of files.")
file_name = directory + getAgentName() + ".pkl"
f = open(file_name, 'wb')
dill.dump(masterAgent.getPolicy(), f)
f.close()
f = open(
directory + "trainingData_" + str(settings['agent_name']) + ".json",
"w")
for key in trainData:
trainData[key] = [float(i) for i in trainData[key]]
json.dump(trainData, f, sort_keys=True, indent=4)
f.close()
if (settings['train_forward_dynamics']):
file_name_dynamics = directory + "forward_dynamics_" + ".pkl"
f = open(file_name_dynamics, 'wb')
dill.dump(masterAgent.getForwardDynamics(), f)
f.close()
print("Delete any plots being used")
gc.collect() #立即释放内存
def trainMetaModel(settingsFileName,
samples=10,
settings=None,
numThreads=1,
hyperSettings=None):
import shutil
import os
result_data = {}
result_data['settings_files'] = []
if (settings is None):
file = open(settingsFileName)
settings = json.load(file)
# print ("Settings: " + str(json.dumps(settings)))
file.close()
print("Running ", samples, " simulation(s) over ", numThreads,
" Thread(s)")
settings_original = copy.deepcopy(settings)
directory_ = getBaseDataDirectory(settings_original)
if not os.path.exists(directory_):
os.makedirs(directory_)
out_file_name = directory_ + "settings.json"
print("Saving settings file with data to: ", out_file_name)
out_file = open(out_file_name, 'w')
out_file.write(json.dumps(settings_original, indent=4))
# file.close()
out_file.close()
sim_settings = []
sim_settingFileNames = []
sim_data = []
for i in range(samples):
settings['data_folder'] = settings_original['data_folder'] + "_" + str(
i)
settings['random_seed'] = int(settings['random_seed']) + (
(int(settings['num_available_threads']) + 1) * i)
## Change some other settings to reduce memory usage and train faster
settings['print_level'] = "hyper_train"
settings['shouldRender'] = False
settings['visualize_learning'] = False
settings['saving_update_freq_num_rounds'] = settings_original[
'saving_update_freq_num_rounds'] * 10
if ('expert_policy_files' in settings):
for j in range(len(settings['expert_policy_files'])):
settings['expert_policy_files'][j] = settings_original[
'expert_policy_files'][j] + "/_" + str(i)
result_data['settings_files'].append(copy.deepcopy(settings))
sim_settings.append(copy.deepcopy(settings))
sim_settingFileNames.append(settingsFileName)
sim_data.append((settingsFileName, copy.deepcopy(settings)))
## Create data directory and copy any desired files to these folders .
if (not (hyperSettings is None)):
# file = open(hyperSettings)
hyper_settings = hyperSettings
# print ("Settings: " + str(json.dumps(settings)))
# file.close()
directory = getDataDirectory(settings)
if not os.path.exists(directory):
os.makedirs(directory)
if ('saved_model_path' in hyperSettings):
print("Copying fd model: ", hyperSettings['saved_model_path'])
# shutil.copy2(hyperSettings['saved_model_path'], directory+"forward_dynamics_"+"_Best_pretrain.pkl" )
shutil.copy2(hyperSettings['saved_model_path'],
directory + getAgentName() + "_Best.pkl")
if ('saved_model_folder' in hyperSettings):
### Copy models from other metamodel simulation
### Purposefully not copying the "Best" model but the last instead
shutil.copy2(
hyperSettings['saved_model_folder'] + "/_" + str(i) + '/' +
settings['model_type'] + '/' + getAgentName() + ".pkl",
directory + getAgentName() + "_Best.pkl")
# p = ThreadPool(numThreads)
p = ProcessingPool(numThreads)
t0 = time.time()
# print ("hyperSettings: ", hyper_settings)
if ((hyperSettings is not None)
and ('testing' in hyper_settings and (hyper_settings['testing']))):
print("Not simulating, this is a testing run:")
else:
result = p.map(trainModelParallel, sim_data)
t1 = time.time()
print("Meta model training complete in " +
str(datetime.timedelta(seconds=(t1 - t0))) + " seconds")
# print (result)
result_data['sim_time'] = "Meta model training complete in " + str(
datetime.timedelta(seconds=(t1 - t0))) + " seconds"
result_data['raw_sim_time_in_seconds'] = t1 - t0
result_data['Number_of_simulations_sampled'] = samples
result_data['Number_of_threads_used'] = numThreads
return result_data
def evaluateModelRender(settings_file_name, runLastModel=False):
settings = getSettings(settings_file_name)
# settings['shouldRender'] = True
import os
os.environ['THEANO_FLAGS'] = "mode=FAST_RUN,device=" + settings[
'training_processor_type'] + ",floatX=" + settings['float_type']
from util.SimulationUtil import validateSettings, createEnvironment, createRLAgent, createActor, getAgentName
from util.SimulationUtil import getDataDirectory, createForwardDynamicsModel, getAgentName
from util.ExperienceMemory import ExperienceMemory
from model.LearningAgent import LearningAgent, LearningWorker
from RLVisualize import RLVisualize
from NNVisualize import NNVisualize
model_type = settings["model_type"]
directory = getDataDirectory(settings)
rounds = settings["rounds"]
epochs = settings["epochs"]
# num_states=settings["num_states"]
epsilon = settings["epsilon"]
discount_factor = settings["discount_factor"]
# max_reward=settings["max_reward"]
batch_size = settings["batch_size"]
state_bounds = np.array(settings['state_bounds'])
action_space_continuous = settings["action_space_continuous"]
discrete_actions = np.array(settings['discrete_actions'])
num_actions = discrete_actions.shape[0]
reward_bounds = np.array(settings["reward_bounds"])
action_space_continuous = settings['action_space_continuous']
if action_space_continuous:
action_bounds = np.array(settings["action_bounds"], dtype=float)
print("Sim config file name: " + str(settings["sim_config_file"]))
### Using a wrapper for the type of actor now
if action_space_continuous:
experience = ExperienceMemory(len(state_bounds[0]),
len(action_bounds[0]),
settings['expereince_length'],
continuous_actions=True,
settings=settings)
else:
experience = ExperienceMemory(len(state_bounds[0]), 1,
settings['expereince_length'])
# actor = ActorInterface(discrete_actions)
actor = createActor(str(settings['environment_type']), settings,
experience)
masterAgent = LearningAgent(n_in=len(state_bounds[0]),
n_out=len(action_bounds[0]),
state_bounds=state_bounds,
action_bounds=action_bounds,
reward_bound=reward_bounds,
settings_=settings)
# c = characterSim.Configuration("../data/epsilon0Config.ini")
if (runLastModel == True):
file_name = directory + getAgentName() + ".pkl"
else:
file_name = directory + getAgentName() + "_Best.pkl"
f = open(file_name, 'rb')
model = dill.load(f)
f.close()
if (settings['train_forward_dynamics']):
file_name_dynamics = directory + "forward_dynamics_" + "_Best.pkl"
# file_name=directory+getAgentName()+".pkl"
f = open(file_name_dynamics, 'rb')
forwardDynamicsModel = dill.load(f)
f.close()
if (settings["use_transfer_task_network"]):
task_directory = getTaskDataDirectory(settings)
file_name = directory + getAgentName() + "_Best.pkl"
f = open(file_name, 'rb')
taskModel = dill.load(f)
f.close()
# copy the task part from taskModel to model
print("Transferring task portion of model.")
model.setTaskNetworkParameters(taskModel)
# this is the process that selects which game to play
sim_index = 0
if ('override_sim_env_id' in settings
and (settings['override_sim_env_id'] != False)):
sim_index = settings['override_sim_env_id']
exp = createEnvironment(settings["sim_config_file"],
settings['environment_type'],
settings,
render=True,
index=sim_index)
if (settings['train_forward_dynamics']):
# actor.setForwardDynamicsModel(forwardDynamicsModel)
forwardDynamicsModel.setActor(actor)
masterAgent.setForwardDynamics(forwardDynamicsModel)
# forwardDynamicsModel.setEnvironment(exp)
# actor.setPolicy(model)
exp.setActor(actor)
exp.getActor().init()
exp.init()
exp.generateValidationEnvironmentSample(0)
expected_value_viz = None
if (settings['visualize_expected_value']):
expected_value_viz = NNVisualize(title=str("Expected Value") +
" with " +
str(settings["model_type"]),
settings=settings)
expected_value_viz.setInteractive()
expected_value_viz.init()
criticLosses = []
masterAgent.setSettings(settings)
masterAgent.setExperience(experience)
masterAgent.setPolicy(model)
"""
mean_reward, std_reward, mean_bellman_error, std_bellman_error, mean_discount_error, std_discount_error, mean_eval, std_eval = evalModel(actor, exp, masterAgent, discount_factor, anchors=_anchors[:settings['eval_epochs']],
action_space_continuous=action_space_continuous, settings=settings, print_data=True, evaluation=True,
visualizeEvaluation=expected_value_viz)
# simEpoch(exp, model, discount_factor=discount_factor, anchors=_anchors[:settings['eval_epochs']][9], action_space_continuous=True, settings=settings, print_data=True, p=0.0, validation=True)
"""
"""
workers = []
input_anchor_queue = Queue(settings['queue_size_limit'])
output_experience_queue = Queue(settings['queue_size_limit'])
for process in range(settings['num_available_threads']):
# this is the process that selects which game to play
exp = characterSim.Experiment(c)
if settings['environment_type'] == 'pendulum_env_state':
print ("Using Environment Type: " + str(settings['environment_type']))
exp = PendulumEnvState(exp)
elif settings['environment_type'] == 'pendulum_env':
print ("Using Environment Type: " + str(settings['environment_type']))
exp = PendulumEnv(exp)
else:
print ("Invalid environment type: " + str(settings['environment_type']))
sys.exit()
exp.getActor().init()
exp.init()
w = SimWorker(input_anchor_queue, output_experience_queue, exp, model, discount_factor, action_space_continuous=action_space_continuous,
settings=settings, print_data=False, p=0.0, validation=True)
w.start()
workers.append(w)
mean_reward, std_reward, mean_bellman_error, std_bellman_error, mean_discount_error, std_discount_error = evalModelParrallel(
input_anchor_queue, output_experience_queue, discount_factor, anchors=_anchors[:settings['eval_epochs']], action_space_continuous=action_space_continuous, settings=settings)
for w in workers:
input_anchor_queue.put(None)
"""
# print ("Average Reward: " + str(mean_reward))
exp.getActor().initEpoch()
exp.initEpoch()
fps = 30
state_ = exp.getState()
action_ = np.array(masterAgent.predict(state_, evaluation_=True),
dtype='float64')
exp.updateAction(action_)
sim = SimContainer(exp, masterAgent, settings, expected_value_viz)
sim._grad_sum = np.zeros_like(state_)
# glutInitWindowPosition(x, y);
# glutInitWindowSize(width, height);
# glutCreateWindow("PyODE Ragdoll Simulation")
# set GLUT callbacks
glutKeyboardFunc(sim.onKey)
## This works because GLUT in C++ uses the same global context (singleton) as the one in python
glutTimerFunc(int(1000.0 / fps), sim.animate, 0) # 30 fps?
# glutIdleFunc(animate)
# enter the GLUT event loop
glutMainLoop()
def __init__(self, model, n_in, n_out, state_bounds, action_bounds,
reward_bound, settings_):
super(Distillation,
self).__init__(model, n_in, n_out, state_bounds, action_bounds,
reward_bound, settings_)
# create a small convolutional neural network
### Load expert policy files
self._expert_policies = []
file_name_ = ""
for i in range(len(self.getSettings()['expert_policy_files'])):
file_name = self.getSettings(
)['expert_policy_files'][i] + '/' + self.getSettings(
)['model_type'] + '/' + getAgentName() + '.pkl'
if (file_name_ == file_name):
## To help save memory when experts are the same
# model_ = self._expert_policies[len(self._expert_policies)-1]
self._expert_policies.append(model_)
else:
print("Loading pre compiled network: ", file_name)
f = open(file_name, 'rb')
model_ = dill.load(f)
# model.setSettings(settings)
f.close()
self._expert_policies.append(model_)
file_name_ = file_name
self._actor_buffer_states = []
self._actor_buffer_result_states = []
self._actor_buffer_actions = []
self._actor_buffer_rewards = []
self._actor_buffer_falls = []
self._actor_buffer_diff = []
self._NotFallen = T.bcol("Not_Fallen")
## because float64 <= float32 * int32, need to use int16 or int8
self._NotFallen.tag.test_value = np.zeros((self._batch_size, 1),
dtype=np.dtype('int8'))
self._NotFallen_shared = theano.shared(np.zeros((self._batch_size, 1),
dtype='int8'),
broadcastable=(False, True))
self._tmp_diff = T.col("Tmp_Diff")
self._tmp_diff.tag.test_value = np.zeros(
(self._batch_size, 1),
dtype=np.dtype(self.getSettings()['float_type']))
self._tmp_diff_shared = theano.shared(np.zeros(
(self._batch_size, 1), dtype=self.getSettings()['float_type']),
broadcastable=(False, True))
"""
self._target_shared = theano.shared(
np.zeros((self._batch_size, 1), dtype='float64'),
broadcastable=(False, True))
"""
self._critic_regularization_weight = self.getSettings(
)["critic_regularization_weight"]
self._critic_learning_rate = self.getSettings()["critic_learning_rate"]
## Target network
self._modelTarget = copy.deepcopy(model)
self._q_valsA = lasagne.layers.get_output(
self._model.getCriticNetwork(),
self._model.getStateSymbolicVariable(),
deterministic=True)
self._q_valsA_drop = lasagne.layers.get_output(
self._model.getCriticNetwork(),
self._model.getStateSymbolicVariable(),
deterministic=False)
self._q_valsNextState = lasagne.layers.get_output(
self._model.getCriticNetwork(),
self._model.getResultStateSymbolicVariable(),
deterministic=True)
self._q_valsTargetNextState = lasagne.layers.get_output(
self._modelTarget.getCriticNetwork(),
self._model.getResultStateSymbolicVariable(),
deterministic=True)
self._q_valsTarget = lasagne.layers.get_output(
self._modelTarget.getCriticNetwork(),
self._model.getStateSymbolicVariable(),
deterministic=True)
self._q_valsTarget_drop = lasagne.layers.get_output(
self._modelTarget.getCriticNetwork(),
self._model.getStateSymbolicVariable(),
deterministic=False)
self._q_valsActA = lasagne.layers.get_output(
self._model.getActorNetwork(),
self._model.getStateSymbolicVariable(),
deterministic=True)
self._q_valsActTarget = lasagne.layers.get_output(
self._modelTarget.getActorNetwork(),
self._model.getStateSymbolicVariable(),
deterministic=True)
self._q_valsActA_drop = lasagne.layers.get_output(
self._model.getActorNetwork(),
self._model.getStateSymbolicVariable(),
deterministic=False)
self._q_func = self._q_valsA
self._q_funcTarget = self._q_valsTarget
self._q_func_drop = self._q_valsA_drop
self._q_funcTarget_drop = self._q_valsTarget_drop
self._q_funcAct = self._q_valsActA
self._q_funcAct_drop = self._q_valsActA_drop
# self._target = (self._model.getRewardSymbolicVariable() + (np.array([self._discount_factor] ,dtype=np.dtype(self.getSettings()['float_type']))[0] * self._q_valsTargetNextState )) * self._NotFallen
# self._target = self._model.getRewardSymbolicVariable() + ((self._discount_factor * self._q_valsTargetNextState ) * self._NotFallen) + (self._NotFallen - 1)
self._target = self._model.getRewardSymbolicVariable() + (
self._discount_factor * self._q_valsTargetNextState)
self._diff = self._target - self._q_func
self._diff_drop = self._target - self._q_func_drop
# loss = 0.5 * self._diff ** 2
loss = T.pow(self._diff, 2)
self._loss = T.mean(loss)
self._loss_drop = T.mean(0.5 * self._diff_drop**2)
self._params = lasagne.layers.helper.get_all_params(
self._model.getCriticNetwork())
self._actionParams = lasagne.layers.helper.get_all_params(
self._model.getActorNetwork())
self._givens_ = {
self._model.getStateSymbolicVariable():
self._model.getStates(),
self._model.getResultStateSymbolicVariable():
self._model.getResultStates(),
self._model.getRewardSymbolicVariable():
self._model.getRewards(),
# self._NotFallen: self._NotFallen_shared
# self._model.getActionSymbolicVariable(): self._actions_shared,
}
self._actGivens = {
self._model.getStateSymbolicVariable():
self._model.getStates(),
# self._model.getResultStateSymbolicVariable(): self._model.getResultStates(),
# self._model.getRewardSymbolicVariable(): self._model.getRewards(),
self._model.getActionSymbolicVariable():
self._model.getActions(),
# self._NotFallen: self._NotFallen_shared
self._tmp_diff:
self._tmp_diff_shared
}
self._critic_regularization = (
self._critic_regularization_weight *
lasagne.regularization.regularize_network_params(
self._model.getCriticNetwork(), lasagne.regularization.l2))
self._actor_regularization = (
(self._regularization_weight *
lasagne.regularization.regularize_network_params(
self._model.getActorNetwork(), lasagne.regularization.l2)))
if (self.getSettings()['use_previous_value_regularization']):
self._actor_regularization = self._actor_regularization + (
(self.getSettings()['previous_value_regularization_weight']) *
change_penalty(self._model.getActorNetwork(),
self._modelTarget.getActorNetwork()))
elif ('regularization_type' in self.getSettings() and
(self.getSettings()['regularization_type'] == 'KL_Divergence')):
self._kl_firstfixed = T.mean(
kl(
self._q_valsActTarget,
T.ones_like(self._q_valsActTarget) *
self.getSettings()['exploration_rate'], self._q_valsActA,
T.ones_like(self._q_valsActA) *
self.getSettings()['exploration_rate'],
self._action_length))
#self._actor_regularization = (( self._KL_Weight ) * self._kl_firstfixed ) + (10*(self._kl_firstfixed>self.getSettings()['kl_divergence_threshold'])*
# T.square(self._kl_firstfixed-self.getSettings()['kl_divergence_threshold']))
self._actor_regularization = (self._kl_firstfixed) * (
self.getSettings()['kl_divergence_threshold'])
print("Using regularization type : ",
self.getSettings()['regularization_type'])
# SGD update
# self._updates_ = lasagne.updates.rmsprop(self._loss, self._params, self._learning_rate, self._rho,
# self._rms_epsilon)
self._value_grad = T.grad(self._loss + self._critic_regularization,
self._params)
## Clipping the max gradient
"""
for x in range(len(self._value_grad)):
self._value_grad[x] = T.clip(self._value_grad[x] , -0.1, 0.1)
"""
if (self.getSettings()['optimizer'] == 'rmsprop'):
print("Optimizing Value Function with ",
self.getSettings()['optimizer'], " method")
self._updates_ = lasagne.updates.rmsprop(self._value_grad,
self._params,
self._learning_rate,
self._rho,
self._rms_epsilon)
elif (self.getSettings()['optimizer'] == 'momentum'):
print("Optimizing Value Function with ",
self.getSettings()['optimizer'], " method")
self._updates_ = lasagne.updates.momentum(
self._value_grad,
self._params,
self._critic_learning_rate,
momentum=self._rho)
elif (self.getSettings()['optimizer'] == 'adam'):
print("Optimizing Value Function with ",
self.getSettings()['optimizer'], " method")
self._updates_ = lasagne.updates.adam(self._value_grad,
self._params,
self._critic_learning_rate,
beta1=0.9,
beta2=0.9,
epsilon=self._rms_epsilon)
elif (self.getSettings()['optimizer'] == 'adagrad'):
print("Optimizing Value Function with ",
self.getSettings()['optimizer'], " method")
self._updates_ = lasagne.updates.adagrad(
self._value_grad,
self._params,
self._critic_learning_rate,
epsilon=self._rms_epsilon)
else:
print("Unknown optimization method: ",
self.getSettings()['optimizer'])
sys.exit(-1)
## TD update
"""
if (self.getSettings()['optimizer'] == 'rmsprop'):
self._updates_ = lasagne.updates.rmsprop(T.mean(self._q_func) + self._critic_regularization, self._params,
self._critic_learning_rate * -T.mean(self._diff), self._rho, self._rms_epsilon)
elif (self.getSettings()['optimizer'] == 'momentum'):
self._updates_ = lasagne.updates.momentum(T.mean(self._q_func) + self._critic_regularization, self._params,
self._critic_learning_rate * -T.mean(self._diff), momentum=self._rho)
elif ( self.getSettings()['optimizer'] == 'adam'):
self._updates_ = lasagne.updates.adam(T.mean(self._q_func), self._params,
self._critic_learning_rate * -T.mean(self._diff), beta1=0.9, beta2=0.999, epsilon=1e-08)
else:
print ("Unknown optimization method: ", self.getSettings()['optimizer'])
sys.exit(-1)
"""
## Need to perform an element wise operation or replicate _diff for this to work properly.
# self._actDiff = theano.tensor.elemwise.Elemwise(theano.scalar.mul)((self._model.getActionSymbolicVariable() - self._q_valsActA), theano.tensor.tile((self._diff * (1.0/(1.0-self._discount_factor))), self._action_length)) # Target network does not work well here?
self._actDiff = (self._model.getActionSymbolicVariable() -
self._q_valsActA_drop)
# self._actDiff = ((self._model.getActionSymbolicVariable() - self._q_valsActA)) # Target network does not work well here?
# self._actDiff_drop = ((self._model.getActionSymbolicVariable() - self._q_valsActA_drop)) # Target network does not work well here?
## This should be a single column vector
# self._actLoss_ = theano.tensor.elemwise.Elemwise(theano.scalar.mul)(( T.transpose(T.sum(T.pow(self._actDiff, 2),axis=1) )), (self._diff * (1.0/(1.0-self._discount_factor))))
# self._actLoss_ = theano.tensor.elemwise.Elemwise(theano.scalar.mul)(( T.reshape(T.sum(T.pow(self._actDiff, 2),axis=1), (self._batch_size, 1) )),
# (self._tmp_diff * (1.0/(1.0-self._discount_factor)))
# self._actLoss_ = (T.mean(T.pow(self._actDiff, 2),axis=1))
self._actLoss_ = theano.tensor.elemwise.Elemwise(theano.scalar.mul)(
(T.mean(T.pow(self._actDiff, 2), axis=1)), (self._tmp_diff))
# self._actLoss = T.sum(self._actLoss)/float(self._batch_size)
self._actLoss = T.mean(self._actLoss_)
# self._actLoss_drop = (T.sum(0.5 * self._actDiff_drop ** 2)/float(self._batch_size)) # because the number of rows can shrink
# self._actLoss_drop = (T.mean(0.5 * self._actDiff_drop ** 2))
self._policy_grad = T.grad(self._actLoss + self._actor_regularization,
self._actionParams)
## Clipping the max gradient
"""
for x in range(len(self._policy_grad)):
self._policy_grad[x] = T.clip(self._policy_grad[x] , -0.5, 0.5)
"""
if (self.getSettings()['optimizer'] == 'rmsprop'):
self._actionUpdates = lasagne.updates.rmsprop(
self._policy_grad, self._actionParams, self._learning_rate,
self._rho, self._rms_epsilon)
elif (self.getSettings()['optimizer'] == 'momentum'):
self._actionUpdates = lasagne.updates.momentum(self._policy_grad,
self._actionParams,
self._learning_rate,
momentum=self._rho)
elif (self.getSettings()['optimizer'] == 'adam'):
self._actionUpdates = lasagne.updates.adam(
self._policy_grad,
self._actionParams,
self._learning_rate,
beta1=0.9,
beta2=0.999,
epsilon=self._rms_epsilon)
elif (self.getSettings()['optimizer'] == 'adagrad'):
self._actionUpdates = lasagne.updates.adagrad(
self._policy_grad,
self._actionParams,
self._learning_rate,
epsilon=self._rms_epsilon)
else:
print("Unknown optimization method: ",
self.getSettings()['optimizer'])
# actionUpdates = lasagne.updates.rmsprop(T.mean(self._q_funcAct_drop) +
# (self._regularization_weight * lasagne.regularization.regularize_network_params(
# self._model.getActorNetwork(), lasagne.regularization.l2)), actionParams,
# self._learning_rate * 0.5 * (-T.sum(actDiff_drop)/float(self._batch_size)), self._rho, self._rms_epsilon)
self._givens_grad = {
self._model.getStateSymbolicVariable(): self._model.getStates(),
# self._model.getResultStateSymbolicVariable(): self._model.getResultStates(),
# self._model.getRewardSymbolicVariable(): self._model.getRewards(),
# self._model.getActionSymbolicVariable(): self._model.getActions(),
}
### Noisey state updates
# self._target = (self._model.getRewardSymbolicVariable() + (np.array([self._discount_factor] ,dtype=np.dtype(self.getSettings()['float_type']))[0] * self._q_valsTargetNextState )) * self._NotFallen
# self._target_dyna = theano.gradient.disconnected_grad(self._q_func)
## Bellman error
self._bellman = self._target - self._q_funcTarget
# self._target = self._model.getRewardSymbolicVariable() + (self._discount_factor * self._q_valsTargetNextState )
### Give v(s') the next state and v(s) (target) the current state
self._diff_adv = (self._discount_factor *
self._q_func) - (self._q_valsTargetNextState)
self._diff_adv_givens = {
self._model.getStateSymbolicVariable():
self._model.getResultStates(),
self._model.getResultStateSymbolicVariable():
self._model.getStates(),
}
Distillation.compile(self)
def __init__(self, model, n_in, n_out, state_bounds, action_bounds,
reward_bound, settings_):
super(Distillation,
self).__init__(model, n_in, n_out, state_bounds, action_bounds,
reward_bound, settings_)
# create a small convolutional neural network
### Load expert policy files
self._expert_policies = []
file_name_ = ""
for i in range(len(self.getSettings()['expert_policy_files'])):
file_name = self.getSettings(
)['expert_policy_files'][i] + '/' + self.getSettings(
)['model_type'] + '/' + getAgentName() + '.pkl'
if (file_name_ == file_name):
## To help save memory when experts are the same
self._expert_policies.append(model_)
else:
print("Loading pre compiled network: ", file_name)
f = open(file_name, 'rb')
model_ = dill.load(f)
f.close()
self._expert_policies.append(
model_) # expert model, load the 2 expert models
file_name_ = file_name
self._actor_buffer_states = []
self._actor_buffer_result_states = []
self._actor_buffer_actions = []
self._actor_buffer_rewards = []
self._actor_buffer_falls = []
self._actor_buffer_diff = []
self._NotFallen = T.bcol("Not_Fallen")
## because float64 <= float32 * int32, need to use int16 or int8
self._NotFallen.tag.test_value = np.zeros((self._batch_size, 1),
dtype=np.dtype('int8'))
self._NotFallen_shared = theano.shared(np.zeros((self._batch_size, 1),
dtype='int8'),
broadcastable=(False, True))
self._tmp_diff = T.col("Tmp_Diff")
self._tmp_diff.tag.test_value = np.zeros(
(self._batch_size, 1),
dtype=np.dtype(self.getSettings()['float_type']))
self._tmp_diff_shared = theano.shared(
np.zeros((self._batch_size, 1),
dtype=self.getSettings()['float_type']),
broadcastable=(False, True)) #定义一个共享变量，初始值为为0
self._critic_regularization_weight = self.getSettings(
)["critic_regularization_weight"]
self._critic_learning_rate = self.getSettings()["critic_learning_rate"]
## Target network
self._modelTarget = copy.deepcopy(model) # target model 是要更新的模型
self._q_valsA = lasagne.layers.get_output(
self._model.getCriticNetwork(),
self._model.getStateSymbolicVariable(),
deterministic=True) #确定性原始模型的state值输出
self._q_valsA_drop = lasagne.layers.get_output(
self._model.getCriticNetwork(),
self._model.getStateSymbolicVariable(),
deterministic=False) #非确定的state值输出
self._q_valsNextState = lasagne.layers.get_output(
self._model.getCriticNetwork(),
self._model.getResultStateSymbolicVariable(),
deterministic=True) #下一步的state值
self._q_valsTargetNextState = lasagne.layers.get_output(
self._modelTarget.getCriticNetwork(),
self._model.getResultStateSymbolicVariable(),
deterministic=True) #目标模型的下一步的state值
self._q_valsTarget = lasagne.layers.get_output(
self._modelTarget.getCriticNetwork(),
self._model.getStateSymbolicVariable(),
deterministic=True) #目标模型的state值
self._q_valsTarget_drop = lasagne.layers.get_output(
self._modelTarget.getCriticNetwork(),
self._model.getStateSymbolicVariable(),
deterministic=False) #目标模型的state
self._q_valsActA = lasagne.layers.get_output(
self._model.getActorNetwork(),
self._model.getStateSymbolicVariable(),
deterministic=True)
self._q_valsActTarget = lasagne.layers.get_output(
self._modelTarget.getActorNetwork(),
self._model.getStateSymbolicVariable(),
deterministic=True) #remove the random
self._q_valsActA_drop = lasagne.layers.get_output(
self._model.getActorNetwork(),
self._model.getStateSymbolicVariable(),
deterministic=False) #actor 值
self._q_func = self._q_valsA
self._q_funcTarget = self._q_valsTarget
self._q_func_drop = self._q_valsA_drop
self._q_funcTarget_drop = self._q_valsTarget_drop
self._q_funcAct = self._q_valsActA
self._q_funcAct_drop = self._q_valsActA_drop
self._target = self._model.getRewardSymbolicVariable() + (
self._discount_factor * self._q_valsTargetNextState)
# self._model.getRewardSymbolicVariable() 获取rewards的值getRewards() =self._rewards_shared 从0开始一直更新
self._diff = self._target - self._q_func
self._diff_drop = self._target - self._q_func_drop #更新的模型的reward减去原始模型的critic的输出值
loss = T.pow(self._diff, 2)
self._loss = T.mean(loss) # 两个模型的reward的差值
self._loss_drop = T.mean(0.5 * self._diff_drop**2)
self._params = lasagne.layers.helper.get_all_params(
self._model.getCriticNetwork())
self._actionParams = lasagne.layers.helper.get_all_params(
self._model.getActorNetwork())
self._givens_ = {
self._model.getStateSymbolicVariable():
self._model.getStates(),
self._model.getResultStateSymbolicVariable():
self._model.getResultStates(),
self._model.getRewardSymbolicVariable():
self._model.getRewards()
}
self._actGivens = {
self._model.getStateSymbolicVariable(): self._model.getStates(),
self._model.getActionSymbolicVariable(): self._model.getActions(),
self._tmp_diff: self._tmp_diff_shared
}
self._critic_regularization = (
self._critic_regularization_weight *
lasagne.regularization.regularize_network_params(
self._model.getCriticNetwork(), lasagne.regularization.l2))
self._actor_regularization = (
(self._regularization_weight *
lasagne.regularization.regularize_network_params(
self._model.getActorNetwork(), lasagne.regularization.l2)))
if (self.getSettings()['use_previous_value_regularization']):
self._actor_regularization = self._actor_regularization + (
(self.getSettings()['previous_value_regularization_weight']) *
change_penalty(self._model.getActorNetwork(),
self._modelTarget.getActorNetwork()))
elif ('regularization_type' in self.getSettings() and
(self.getSettings()['regularization_type'] == 'KL_Divergence')):
self._kl_firstfixed = T.mean(
kl(
self._q_valsActTarget,
T.ones_like(self._q_valsActTarget) *
self.getSettings()['exploration_rate'], self._q_valsActA,
T.ones_like(self._q_valsActA) *
self.getSettings()['exploration_rate'],
self._action_length))
self._actor_regularization = (self._kl_firstfixed) * (
self.getSettings()['kl_divergence_threshold'])
print("Using regularization type : ",
self.getSettings()['regularization_type'])
# SGD update
self._value_grad = T.grad(self._loss + self._critic_regularization,
self._params)
if (self.getSettings()['optimizer'] == 'rmsprop'):
print("Optimizing Value Function with ",
self.getSettings()['optimizer'], " method")
self._updates_ = lasagne.updates.rmsprop(self._value_grad,
self._params,
self._learning_rate,
self._rho,
self._rms_epsilon)
elif (self.getSettings()['optimizer'] == 'momentum'):
print("Optimizing Value Function with ",
self.getSettings()['optimizer'], " method")
self._updates_ = lasagne.updates.momentum(
self._value_grad,
self._params,
self._critic_learning_rate,
momentum=self._rho)
elif (self.getSettings()['optimizer'] == 'adam'):
print("Optimizing Value Function with ",
self.getSettings()['optimizer'], " method")
self._updates_ = lasagne.updates.adam(self._value_grad,
self._params,
self._critic_learning_rate,
beta1=0.9,
beta2=0.9,
epsilon=self._rms_epsilon)
elif (self.getSettings()['optimizer'] == 'adagrad'):
print("Optimizing Value Function with ",
self.getSettings()['optimizer'], " method")
self._updates_ = lasagne.updates.adagrad(
self._value_grad,
self._params,
self._critic_learning_rate,
epsilon=self._rms_epsilon)
else:
print("Unknown optimization method: ",
self.getSettings()['optimizer'])
sys.exit(-1)
## TD update
## Need to perform an element wise operation or replicate _diff for this to work properly.
self._actDiff = (self._model.getActionSymbolicVariable() -
self._q_valsActA_drop) # 更新模型的actor的输出减去原始模型的actor值
self._actLoss_ = theano.tensor.elemwise.Elemwise(theano.scalar.mul)(
(T.mean(T.pow(self._actDiff, 2), axis=1)), (self._tmp_diff))
self._actLoss = T.mean(self._actLoss_)
self._policy_grad = T.grad(self._actLoss + self._actor_regularization,
self._actionParams)
## Clipping the max gradient
if (self.getSettings()['optimizer'] == 'rmsprop'):
self._actionUpdates = lasagne.updates.rmsprop(
self._policy_grad, self._actionParams, self._learning_rate,
self._rho, self._rms_epsilon)
elif (self.getSettings()['optimizer'] == 'momentum'):
self._actionUpdates = lasagne.updates.momentum(self._policy_grad,
self._actionParams,
self._learning_rate,
momentum=self._rho)
elif (self.getSettings()['optimizer'] == 'adam'):
self._actionUpdates = lasagne.updates.adam(
self._policy_grad,
self._actionParams,
self._learning_rate,
beta1=0.9,
beta2=0.999,
epsilon=self._rms_epsilon)
elif (self.getSettings()['optimizer'] == 'adagrad'):
self._actionUpdates = lasagne.updates.adagrad(
self._policy_grad,
self._actionParams,
self._learning_rate,
epsilon=self._rms_epsilon)
else:
print("Unknown optimization method: ",
self.getSettings()['optimizer'])
self._givens_grad = {
self._model.getStateSymbolicVariable(): self._model.getStates()
}
## Bellman error
self._bellman = self._target - self._q_funcTarget
### Give v(s') the next state and v(s) (target) the current state
self._diff_adv = (self._discount_factor * self._q_func) - (
self._q_valsTargetNextState
) #\gamma*critic模型的输出-critic模型在下一个状态的输出值
self._diff_adv_givens = {
self._model.getStateSymbolicVariable():
self._model.getResultStates(),
self._model.getResultStateSymbolicVariable():
self._model.getStates(),
}
Distillation.compile(self)