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'
occupancy_reward_type = 'IR_distance'
restore_critic_model_flag=False)
# Learning records
episod_reward_memory = deque(maxlen=10000)
# Train parameters
max_episodes = 50000
max_episode_len = 1000
render_env = False
reward = 0
done = False
start_time = time.time()
try:
for i in range(max_episodes):
observation = env.reset()
ep_reward = 0
for j in range(max_episode_len):
if render_env == True:
env.render()
# Added exploration noise
action = LASAgent.perceive_and_act(observation, reward,
done)
observation, reward, done, info = env.step(action[0])
ep_reward += reward
if done or j == (max_episode_len - 1):
print('| Reward: {:d} | Episode: {:d} '.format(
int(ep_reward), i))
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
exploration_action_noise_type='ou_0.2',
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)
observationForLAS, rewardLAS, done, info = envLAS.step_LAS(actionLAS)
print("Ext_Mot_LASAgent Step: {}, reward: {}".format(i, rewardLAS))
i += 1
observationForLAS, rewardLAS, done, info = envLAS.reset()
for temp in range(10000):
actionLAS = Random_LASAgent.perceive_and_act(observationForLAS, rewardLAS, done)
observationForLAS, rewardLAS, done, info = envLAS.step_LAS(actionLAS)
print("Random_LASAgent Step: {}, reward: {}".format(i, rewardLAS))
i += 1
# -*- coding: utf-8 -*-
"""
Created on Wed May 9 08:52:36 2018
@author: jack.lingheng.meng
"""
from Environment.LASEnv import LASEnv
from LASAgent.RandomLASAgent import RandomLASAgent
if __name__ == '__main__':
# Instantiate environment object
envLAS = LASEnv('127.0.0.1', 19997)
observationForLAS = envLAS.reset()
# Iinstantiate LAS-agent
LASAgent1 = RandomLASAgent(envLAS.observation_space, envLAS.action_space)
# Step counter
i = 1
done = False
rewardLAS = 0
while not done:
actionLAS = LASAgent1.interact(observationForLAS, rewardLAS, done)
observationForLAS, rewardLAS, done, info = envLAS.step(actionLAS)
print("LAS Step: {}, reward: {}".format(i, rewardLAS))
i += 1