def main():
epsilon = 1
env = MetaEnvMulti() # TODO
EPISODES = args['episodes']
a = str(datetime.now()).split('.')[0]
MetaAgent = DQNAgent(state_size=META_STATE_SIZE,
action_size=META_OPTION_SIZE,
hiddenLayers=[75],
dropout=args['dropout'],
activation='relu',
loadname=None,
saveIn=False,
learningRate=args['learning_rate'],
discountFactor=args['discount_factor'])
filename = args['save_folder']
if 'meta-weights' not in args.keys():
filename = "{}{}_Meta_HiddenLayers_{}_Dropout_{}_LearningRate_{}_Gamma_{}_Activation_{}_Episode_{}_single_nn_policy{}.h5".format(
filename, a, str(MetaAgent.hiddenLayers), str(MetaAgent.dropout),
str(MetaAgent.learning_rate), str(MetaAgent.gamma),
MetaAgent.activation, str(EPISODES), args['note_file'])
else:
filename = filename + args['meta_weights']
# See if the user has given the hidden layer configuration for option_agnet
nodes_hidden = [75] # defaulr value
if 'controller_hidden' in args.keys():
controller_hidden_config = args['controller_hidden']
# extract the array from same
nodes_hidden = [
int(node) for node in controller_hidden_config.split('_')
]
# load the agents for the controller , which is single for this case
option_agent: DQNAgent = DQNAgent(
state_size=CONTROLLER_STATE_SIZE,
action_size=CONTROLLER_ACTION_SIZE,
hiddenLayers=nodes_hidden,
dropout=0.000,
activation='relu',
loadname=None,
saveIn=False,
learningRate=0.05,
discountFactor=0.7,
epsilon=0.0) # Not mkaing an agent for the user based actions
option_agent.load(args['controller_weights']
) # Load the weight for al the controller policies
visits = np.zeros([META_OPTION_SIZE
]) # Store the number of Visits of each intentn tyope
batch_size = 64
track = []
i = 0
no_controller_breaks = 0
config_file = '{}{}.txt'.format(
args['config_folder'], a
) # this is the configualtion older containt all the details of the experimernt along with the files names
with open(config_file, 'w') as fil:
fil.write(str(args))
fil.write('\n')
fil.write("meta_policy_file : {}".format(filename))
for episode in range(EPISODES): # Episode
running_meta_reward = 0
[confidence_state, intent_state] = env.reset() #
done = False # Running the meta polciy
while not done: # Meta Policy Epsiode Loop
# print("Round Meta : {}".format(episode)) # Probably not requried
all_options = env.constrain_options()
state = np.concatenate([confidence_state, intent_state])
state = state.reshape([1, META_STATE_SIZE
]) # Converted to appropritate size
meta_start_state = state.copy()
option = MetaAgent.act(state, all_options, epsilon=epsilon)
next_confidence_state = env.meta_step_start(
option) # get the reward at the sub policy level
meta_reward = 0
print("The state : {}\nThe option : {}".format(
meta_start_state, option))
if option == 5: # the user agent option:
pass
else:
#############################################################
# HERE COMES THE PART FOR CONTROLLER EXECUTION
option_completed = False
# make a one hot goal vector
goal_vector = utils.one_hot(option, NO_INTENTS)
i_ = 0
controller_state = np.concatenate(
[next_confidence_state, goal_vector])
controller_state = controller_state.reshape(
1, CONTROLLER_STATE_SIZE)
while not option_completed:
opt_actions = range(
CONTROLLER_ACTION_SIZE
) # Currently it is the while possible actions size
action = option_agent.act(
controller_state, all_act=opt_actions,
epsilon=0) # provide episone for greedy approach
next_confidence_state, _, option_completed = env.controller_step(
option, action)
next_controller_state = np.concatenate(
[next_confidence_state, goal_vector])
next_controller_state = np.reshape(
next_controller_state, [1, CONTROLLER_STATE_SIZE])
# we dont need to store the experience replay memory for the controller policy
controller_state = next_controller_state
i_ += 1
if i_ > args['break_controller_loop']:
no_controller_breaks += 1
break
###############################################
confidence_state, next_confidence_state, intent_state, meta_reward, done = env.meta_step_end2(
option)
meta_end_state = np.concatenate(
[next_confidence_state, intent_state])
meta_end_state = meta_end_state.reshape([1, META_STATE_SIZE])
epsilon = MetaAgent.observe(
(meta_start_state, option, meta_reward, meta_end_state, done),
epsilon=epsilon)
print("The next meta state : {}\n The reward : {}\nEpsilon : {}".
format(meta_end_state, meta_reward, epsilon))
if MetaAgent.memory.tree.total() > batch_size:
MetaAgent.replay()
MetaAgent.rem_rew(meta_reward)
i += 1
running_meta_reward = running_meta_reward + meta_reward
if i % 100 == 0: # calculating different variables to be outputted after every 100 time steps
avr_rew = MetaAgent.avg_rew()
track.append([
str(i) + " " + str(avr_rew) + " " + str(episode) + " " +
str(epsilon)
])
with open("results_" + a + "_.txt", 'w') as fi:
for j in range(0, len(track)):
line = track[j]
fi.write(str(line).strip("[]''") + "\n")
# print(track)
if done:
print(
"episode: {}/{}, score: {}, e's: {}\nNumber of Controller breaks : {}"
.format(episode, EPISODES, running_meta_reward, epsilon,
no_controller_breaks))
print("The state is : ", meta_end_state)
break
confidence_state = next_confidence_state
if episode % 200 == 0:
print("Episodes : {}".format(episode))
# Saving the progress
print("Saving")
# convert this to save model for each policy
MetaAgent.save(filename)
# agent.saveController(fileController)
sleep(0.2)
print("Done Saving You can Now Quit")
sleep(1)
def main():
epsilon = args['epsilon']
env = FlatEnv()
EPISODES = args['episodes']
a = str(datetime.now()).split('.')[0]
hidden_layers = [int(i) for i in args['hidden_layers'].split('_')]
Agent = DQNAgent(state_size=STATES_SIZE,
action_size=ACTION_SIZE,
hiddenLayers=hidden_layers,
dropout=args['dropout'],
activation='relu',
loadname=None,
saveIn=False,
learningRate=args['learning_rate'],
discountFactor=args['discount_factor'])
filename = args['save_folder']
filename = "{}{}_Flat_HiddenLayers_{}_Dropout_{}_LearningRate_{}_Gamma_{}_Activation_{}_Episode_{}_Flat_rl_policy_{}.h5".format(
filename, a, str(Agent.hiddenLayers), str(Agent.dropout),
str(Agent.learning_rate), str(Agent.gamma), Agent.activation,
str(EPISODES), args['note_file'])
batch_size = args['batch_size']
track = []
i = 0
config_file = '{}{}.txt'.format(
args['config_folder'], a
) # this is the configualtion older containt all the details of the experimernt along with the files names
with open(config_file, 'w') as fil:
fil.write(str(args))
fil.write('\n')
fil.write("Flat Policy File : {}".format(filename))
for episode in range(EPISODES): # Episode
running_reward = 0
[confidence_state, intent_state] = env.reset() #
done = False #
while not done: #
#
state = np.concatenate([confidence_state, intent_state])
state = state.reshape([1, STATES_SIZE
]) # Converted to appropritate size
bcolors.printblue("The State : {}".format(state))
intent_set_completed = False # over her the option will mean the consolidated intent sapce of the iteration
i_ = 0
while not intent_set_completed:
all_actions = env.constrain_actions()
action = Agent.act(
state, all_act=all_actions,
epsilon=epsilon) # provide episone for greedy approach
confidence_state, intent_state, reward, intent_set_completed, done = env.step(
action) # the step will be nromal step
next_state = np.concatenate([confidence_state, intent_state])
next_state = np.reshape(next_state, [1, STATES_SIZE])
epsilon = Agent.observe((next_state, action, reward,
next_state, intent_set_completed),
epsilon=epsilon)
if Agent.memory.tree.total() > batch_size:
Agent.replay()
Agent.rem_rew(reward)
running_reward += reward
if i % 100 == 0:
avr_rew = Agent.avg_rew()
track.append([
str(i) + " " + str(avr_rew) + " " + str(episode) +
" " + str(epsilon)
])
with open("results_" + a + "_.txt", 'w') as fi:
for j in range(0, len(track)):
line = track[j]
fi.write(str(line).strip("[]''") + "\n")
# print(track)
if intent_set_completed:
print("Moving to the next set of itnent : {}".format(
intent_state))
break
state = next_state
##############################################
if done:
bcolors.printgreen(
"episode: {}/{}, Reward: {}, e's: {}".format(
episode,
EPISODES,
running_reward,
epsilon,
))
print("The state is : ", state)
break
if episode % 200 == 0:
print("Episodes : {}".format(episode))
# Saving the progress
print("Saving")
# convert this to save model for each policy
Agent.save(filename)
print("Done Saving You can Now Quit")
sleep(0.5)