def plot_cumulative_reward(cumulativeReward):
line, = plt.plot(cumulativeReward)
plt.ion()
plt.show()
plt.pause(0.0001)
if __name__ == '__main__':
with tf.Session() as sess:
#env = gym.make('Pendulum-v0')
#env = gym.make('MountainCarContinuous-v0')
env = LASEnv(IP='127.0.0.1',
Port=19997,
reward_function_type='red_light_dense')
LASAgent = LASAgent_Actor_Critic(
sess,
env,
actor_lr=0.0001,
actor_tau=0.001,
critic_lr=0.0001,
critic_tau=0.001,
gamma=0.99,
minibatch_size=64,
max_episodes=50000,
max_episode_len=1000,
# Exploration Strategies
exploration_action_noise_type='ou_0.2',
from Environment.LASEnv import LASEnv
from LASAgent.InternalEnvOfAgent import InternalEnvOfAgent
from LASAgent.InternalEnvOfCommunity import InternalEnvOfCommunity
# Logging
logging.basicConfig(filename='../ROM_Experiment_results/ROM_experiment_' +
datetime.now().strftime("%Y%m%d-%H%M%S") + '.log',
level=logging.INFO,
format='%(asctime)s:%(levelname)s: %(message)s')
#######################################################################
# Instatiate LAS virtual environment #
#######################################################################
# Instantiate LAS environment object
envLAS = LASEnv('127.0.0.1', 19997, reward_function_type='occupancy')
observation = envLAS.reset()
#######################################################################
# Instatiate LAS-Agent #
#######################################################################
# Note: 1. Set load_pretrained_agent_flag to "True" only when you have
# and want to load pretrained agent.
# 2. Keep observation unchanged if using pretrained agent.
agent_name = 'LAS_Single_Agent'
observation_space = envLAS.observation_space
action_space = envLAS.action_space
observation_space_name = [],
action_space_name = []
x_order_MDP = 5
x_order_MDP_observation_type = 'concatenate_observation'
"""
This script is for interaction between extrinsically motivated LASAgent and
Environment.
Note
----
You should instantiate Environment first, because LASAgent need using Environment
object as parameter to instantiate.
"""
sess = tf.Session()
K.set_session(sess)
# Instantiate LASEnv
envLAS = LASEnv('127.0.0.1', 19997)
# Instantiate Extrinsically Motivated LAS-agent
Ext_Mot_LASAgent = ExtrinsicallyMotivatedLASAgent(envLAS,
sess,
learnFromScratch = True)
# Iinstantiate Random Action LAS-agent
Random_LASAgent = RandomLASAgent(envLAS)
# Step counter
i = 1
observationForLAS, rewardLAS, done, info = envLAS.reset()
#while not done:
for temp in range(10000):
actionLAS = Ext_Mot_LASAgent.perceive_and_act(observationForLAS, rewardLAS, done)
import tensorflow as tf
import numpy as np
import time
from Environment.LASEnv import LASEnv
from LASAgent.RandomLASAgent import RandomLASAgent
from LASAgent.LASAgent_Actor_Critic import LASAgent_Actor_Critic
from Environment.VisitorEnv import VisitorEnv
from VisitorAgent.RedLightExcitedVisitorAgent import RedLightExcitedVisitorAgent
if __name__ == '__main__':
with tf.Session() as sess:
# Instantiate LAS environment object
envLAS = LASEnv('127.0.0.1', 19997, reward_function_type='occupancy')
observation_For_LAS = envLAS.reset()
# Iinstantiate LAS-agent
# LASAgent1 = RandomLASAgent(envLAS)
LASAgent1 = LASAgent_Actor_Critic(
sess,
envLAS,
actor_lr=0.0001,
actor_tau=0.001,
critic_lr=0.0001,
critic_tau=0.001,
gamma=0.99,
minibatch_size=64,
max_episodes=50000,
max_episode_len=1000,
# Exploration Strategies
"""
Created on Tue Aug 7 17:17:08 2018
@author: jack.lingheng.meng
"""
from Environment.LASEnv import LASEnv
from LASAgent.RandomLASAgent import RandomLASAgent
from Environment.VisitorEnv import VisitorEnv
from VisitorAgent.RedLightExcitedVisitorAgent import RedLightExcitedVisitorAgent
if __name__ == '__main__':
# Instantiate LAS environment object
envLAS = LASEnv('127.0.0.1', 19997)
observation_For_LAS = envLAS.reset()
# Iinstantiate LAS-agent
LASAgent1 = RandomLASAgent(envLAS)
# Instantiate visitor environment object
envVisitor = VisitorEnv('127.0.0.1', 19997)
# Instantiate a red light excited visitor0
visitor0 = RedLightExcitedVisitorAgent("Visitor#0")
# Instantiate a red light excited visitor1
visitor1 = RedLightExcitedVisitorAgent("Visitor#1")
# Instantiate a red light excited visitor2
visitor2 = RedLightExcitedVisitorAgent("Visitor#2")
# Instantiate a red light excited visitor2
visitor3 = RedLightExcitedVisitorAgent("Visitor#3")
def plot_cumulative_reward(cumulativeReward):
line, = plt.plot(cumulativeReward)
plt.ion()
plt.show()
plt.pause(0.0001)
if __name__ == '__main__':
with tf.Session() as sess:
#env = gym.make('Pendulum-v0')
#env = gym.make('MountainCarContinuous-v0')
env = LASEnv('127.0.0.1', 19997)
LASAgent = LASAgent_Actor_Critic(sess, env)
#LASAgent.train()
# Learning records
episod_reward_memory = deque(maxlen=10000)
# Train parameters
max_episodes = 50000
max_episode_len = 1000
render_env = False
reward = 0
done = False
for i in range(max_episodes):
'Overall_Summary_Single_Agent')
summary_dir = os.path.join(save_dir, datetime.now().strftime("%Y%m%d-%H%M%S"))
if not os.path.isdir(summary_dir):
os.makedirs(summary_dir)
tf_writer = tf.summary.FileWriter(summary_dir)
# Summarize # of bright light
total_bright_light_number_sum_op,\
total_bright_light_number_sum = _init_summarize_total_bright_light_number()
###############################################################################
#######################################################################
# Instatiate LAS virtual environment #
#######################################################################
# Instantiate LAS environment object
envLAS = LASEnv('127.0.0.1', 19997, reward_function_type='occupancy')
observation = envLAS.reset()
#######################################################################
# Instatiate LAS-Agent #
#######################################################################
# Note: 1. Set load_pretrained_agent_flag to "True" only when you have
# and want to load pretrained agent.
# 2. Keep observation unchanged if using pretrained agent.
agent_name = 'LAS_Single_Agent'
observation_space = envLAS.observation_space
action_space = envLAS.action_space
observation_space_name = [],
action_space_name = []
x_order_MDP = 5
x_order_MDP_observation_type = 'concatenate_observation'