class Experiment_Offline:
def __init__(self):
self.SCENARIO = 'Basic'
# self.SCENARIO = 'Corridor'
# self.SCENARIO = 'Bottleneck'
# self.SCENARIO = 'Crossway'
self.setEnvironment(self.SCENARIO)
self.setNetwork()
self.isTerm = False
self.flag_eval=False
self.EvaluateList = []
self.st_time = time.time()
def setEnvironment(self, SCENARIO):
if SCENARIO == 'Basic':
self.Parser = csim.Parser("Basic")
obs = self.Observe()
if SCENARIO == 'Basic':
self.Scenario = Basic(obs)
def setNetwork(self):
network_dim = []
network_dim.append(4)
network_dim.append(40)
network_dim.append(2)
self.Algorithm = DDPG(network_dim)
def Observe(self):
obs = self.Parser.Observe()
obs = self.convert_to_numpy(obs)
return obs
def Execute(self, action_type, run_type):
obs = self.Observe()
action = self.Algorithm.Action(obs, action_type, run_type)
action = self.convert_to_action_double(action)
memory = self.Parser.Step(action, run_type == 'TEST')
return obs, action, memory
def Update(self, action_type, obs, action, memory):
self.Algorithm.addMemory(action_type=='GREEDY', obs, action, memory['obs'], memory['reward'], memory['isTerm']);
if action_type == 'ACTOR':
self.Algorithm.Update()
def WarmUp(self):
print "\n\n"
print "start time is ... ", strftime("%Y/%m/%d_%I:%M", localtime())
print "Warming up....."
print "========================================="
self.warmup_iter = cst.WARMUP_ITERATION
for i in range(self.warmup_iter):
if self.warmup_iter<10 or i%(self.warmup_iter/10)==0:
print "Warmup Generation...\t", (i / (float)(self.warmup_iter))*100,"%"
self.isTerm = False
self.Parser.Reset(-1)
while not self.isTerm:
obs, action, memory = self.Execute(action_type='GREEDY', run_type='TRAIN')
memory['obs'] = self.convert_to_numpy(memory['obs'])
# print "p: ", memory['obs']['agent'][0]['p']
# print "d: ", memory['obs']['agent'][0]['d']
if memory['isTerm']:
self.isTerm = True
self.Update('GREEDY', obs, action, memory)
print "========================================="
print "Warmup Done!"
print "Replay Memory Size : \t", self.Algorithm.rm.getMemorySize()
print "Total Warmup Time : \t", time.time()-self.st_time, "seconds"
return
def Train(self):
self.st_time = time.time()
self.train_iter = cst.TRAIN_ITERATION
print "Training..."
print "Iteration : ", self.train_iter
print "========================================="
step_count=0
for iteration in range(1, self.train_iter):
if self.isTerm:
print "Reset New Episode : ", step_count," Steps"
step_count=0
self.Algorithm.expl_rate_decay()
self.isTerm = False
if self.flag_eval:
self.episode_evaluation()
self.flag_eval=False
else:
self.Parser.Reset(-1)
obs, action, memory = self.Execute(action_type='ACTOR', run_type='TRAIN')
memory['obs'] = self.convert_to_numpy(memory['obs'])
self.Update('ACTOR', obs, action, memory)
if memory['isTerm']:
self.isTerm = memory['isTerm']
step_count+=1
if self.train_iter<10 or iteration%(self.train_iter/10) == 0:
print "\n", iteration/(float)(self.train_iter/10)*10, "% Done...\n"
if iteration % 400 == 0:
self.flag_eval=True
training_time = time.time() - self.st_time
self.Algorithm.save(FLAG_M_REPLAY_SAVE, training_time, self.EvaluateList)
self.learning_graph()
print "========================================="
print "Training Done!"
print "Total Training Time : \t", training_time, "seconds"
return
def learning_graph(self):
plt.plot(self.EvaluateList)
plt.show()
def episode_evaluation(self):
evaluation_num = cst.EVALUTAION_SET
if self.SCENARIO == 'Circle':
evaluation_num = cst.AGENT_COUNT
step_count = 0
avg_reward = 0
total_reward = 0
for i in range(evaluation_num):
self.Parser.Reset(i)
for j in range(150):
obs, action, memory = self.Execute(action_type='ACTOR', run_type='TRAIN')
total_reward += memory['reward']
step_count += 1
if memory['isTerm']:
break
avg_reward = total_reward / float(step_count)
self.EvaluateList.append(avg_reward)
print "======================================="
print "Avg Reward : ", avg_reward
print "======================================="
def convert_to_numpy(self, obs):
agent_num = len(obs['agent'])
for i in range(agent_num):
obs['agent'][i]['v'] = obs['agent'][i]['v'][0]
obs['agent'][i]['p'] = np.array(obs['agent'][i]['p'])
obs['agent'][i]['q'] = np.array(obs['agent'][i]['q'])
obs['agent'][i]['d'] = np.array(obs['agent'][i]['d'])
obs['agent'][i]['front'] = obs['agent'][i]['front'][0]
obs['agent'][i]['delta'] = np.array(obs['agent'][i]['delta'])
obs['agent'][i]['d_map'] = np.array(obs['agent'][i]['d_map'])
obstacle_num = len(obs['obstacle'])
for i in range(obstacle_num):
obs['obstacle'][i]['p'] = np.array(obs['obstacle'][i]['p'])
return obs
def convert_to_action_double(self, action):
action_len = len(action)
for i in range(action_len):
action[i]['theta'] = float(action[i]['theta'])
action[i]['velocity'] = float(action[i]['velocity'])
return action
class Experiment:
def __init__(self, WIDTH=1260, HEIGHT=680):
self.WIDTH = WIDTH
self.HEIGHT = HEIGHT
self.initGL()
self.initFlag()
SCENARIO = 'Basic'
# SCENARIO = 'Corridor'
# SCENARIO = 'Bottleneck'
# SCENARIO = 'Crossway'
self.setEnvironment(SCENARIO)
self.setNetwork()
if FLAG_USE_RECENT_CKPT:
print "Load Network"
self.load_network()
else:
print "New Network"
if FLAG_WARMUP_FOR_TRAINING:
self.WarmUp()
self.isTerm = False
self.timer_func()
glutMainLoop()
def initGL(self):
argv = sys.argv
glutInit(argv)
glutInitWindowPosition(0, 0)
glutInitWindowSize(self.WIDTH, self.HEIGHT)
glutInitDisplayMode(GLUT_DOUBLE | GLUT_RGB | GLUT_DEPTH)
self.windowID = glutCreateWindow("Crowd Simulation")
glutDisplayFunc(self.display)
glutReshapeFunc(self.reshape)
glutKeyboardFunc(self.keyCB)
def initFlag(self):
self.flag = {}
self.flag['quit'] = False
self.flag['play'] = False
self.flag['train'] = False
self.flag['greedy'] = False
self.flag['step'] = False
self.flag['weight'] = False
self.flag['depth'] = False
self.flag['record'] = False
self.flag['replay'] = False
self.flag['trajectory'] = False
def setEnvironment(self, SCENARIO):
if SCENARIO == 'Basic':
self.Parser = csim.Parser("Basic")
elif SCENARIO == 'Corridor':
self.Parser = csim.Parser("Corridor")
elif SCENARIO == 'Bottleneck':
self.Parser = csim.Parser("Bottleneck")
elif SCENARIO == 'Crossway':
self.Parser = csim.Parser("Crossway")
obs = self.Observe()
if SCENARIO == 'Basic':
self.Scenario = Basic(obs)
# elif SCENARIO == 'Corridor':
# self.Scenario = Corridor(obs)
# elif SCENARIO == 'Bottleneck':
# self.Scenario = Bottleneck(obs)
# elif SCENARIO == 'Crossway':
# self.Scenario = Crossway(obs)
def setNetwork(self):
network_dim = []
network_dim.append(4)
network_dim.append(40)
network_dim.append(2)
self.Algorithm = DDPG(network_dim)
def Reset(self):
self.flag['play'] = False
self.flag['train'] = False
self.flag['record'] = False
self.flag['replay'] = False
self.flag['greedy'] = False
self.Parser.Reset(-1)
obs = self.Observe()
self.Scenario.setObjectData(obs)
def Observe(self):
obs = self.Parser.Observe()
obs = self.convert_to_numpy(obs)
return obs
def Execute(self, action_type, run_type):
obs = self.Observe()
action = self.Algorithm.Action(obs, action_type, run_type)
action = self.convert_to_action_double(action)
memory = self.Parser.Step(action, run_type == 'TEST')
return obs, action, memory
def Update(self, action_type, obs, action, memory):
self.Algorithm.addMemory(action_type == 'GREEDY', obs, action,
memory['obs'], memory['reward'],
memory['isTerm'])
if action_type == 'ACTOR':
self.Algorithm.Update()
def WarmUp(self):
print "Warming up..."
self.warmup_iter = cst.WARMUP_ITERATION
for i in range(self.warmup_iter):
if self.warmup_iter < 10 or i % (self.warmup_iter / 10) == 0:
log("Warmup Generation...\t" +
str((i / (float)(self.warmup_iter)) * 100) + "%")
self.isTerm = False
self.Parser.Reset(-1)
while not self.isTerm:
obs, action, memory = self.Execute(action_type='GREEDY',
run_type='TRAIN')
memory['obs'] = self.convert_to_numpy(memory['obs'])
if memory['isTerm']:
self.isTerm = True
self.Scenario.setObjecytData(memory['obs'])
self.Update('GREEDY', obs, action, memory)
self.flag['warmup'] = False
self.Parser.Reset(-1)
print "Warmup Done!"
def convert_to_numpy(self, obs):
agent_num = len(obs['agent'])
for i in range(agent_num):
obs['agent'][i]['d_map'] = np.array(obs['agent'][i]['d_map'])
obs['agent'][i]['d'] = np.array(obs['agent'][i]['d'])
obs['agent'][i]['delta'] = np.array(obs['agent'][i]['delta'])
obs['agent'][i]['q'] = np.array(obs['agent'][i]['q'])
obs['agent'][i]['p'] = np.array(obs['agent'][i]['p'])
obs['agent'][i]['v'] = obs['agent'][i]['v'][0]
obs['agent'][i]['front'] = obs['agent'][i]['front'][0]
obs['agent'][i]['color'] = np.array(obs['agent'][i]['color'])
obstacle_num = len(obs['obstacle'])
for i in range(obstacle_num):
obs['obstacle'][i]['p'] = np.array(obs['obstacle'][i]['p'])
return obs
def convert_to_action_double(self, action):
action_len = len(action)
for i in range(action_len):
action[i]['theta'] = float(action[i]['theta'])
action[i]['velocity'] = float(action[i]['velocity'])
return action
def timer_func(self, fps=120):
if self.flag['replay']:
fps = 40
self.frame += 1
else:
fps = 120
self.frame = 0
if self.flag['train']:
if self.isTerm:
print "New Episode"
self.Algorithm.expl_rate_decay()
self.isTerm = False
obs, action, memory = self.Execute(action_type='ACTOR',
run_type='TRAIN')
memory['obs'] = self.convert_to_numpy(memory['obs'])
self.Scenario.setObjectData(memory['obs'])
if memory['isTerm']:
self.Parser.Reset(-1)
self.Update('ACTOR', obs, action, memory)
elif self.flag['play']:
if self.flag['greedy']:
obs, action, memory = self.Execute(action_type='GREEDY',
run_type='TEST')
else:
obs, action, memory = self.Execute(action_type='ACTOR',
run_type='TEST')
self.Scenario.setObjectData(memory['obs'])
if memory['isTerm']:
self.flag['play'] = False
print "Scenario Ended"
if self.flag['step']:
self.flag['play'] = not self.flag['play']
self.flag['step'] = False
self.Scenario.record(self.flag['record'])
glutPostRedisplay()
glutTimerFunc(int(1000 / fps), self.timer_func, fps)
def load_network(self, m_replay=False):
self.Algorithm.load_network(type='actor')
self.Algorithm.load_network(type='critic')
if m_replay:
self.Algorithm.load_memory()
self.Algorithm.load_eval()
def save_network(self, m_replay=False, training_time=0, eval_list=None):
self.Algorithm.save(m_replay, training_time, eval_list)
def mouseCB(self, button, state, x, y):
pass
def keyCB(self, key, x, y):
if key:
if key == 'q':
self.flag['quit'] = True
glutDestroyWindow(self.windowID)
elif key == 'd':
if self.flag['train']:
print "Stopped Training...!"
self.save_network()
else:
print "Start Training...!"
self.flag['train'] = not self.flag['train']
self.flag['play'] = False
elif key == 's':
if self.flag['record']:
print "STOP record"
else:
print "START record"
self.flag['record'] = not self.flag['record']
elif key == 'e':
self.flag['depth'] = not self.flag['depth']
elif self.flag['train']:
print "Training... press 'd' to stop training"
elif key == ' ':
if self.flag['play']:
print "STOP"
else:
print "PLAY"
self.flag['play'] = not self.flag['play']
self.flag['greedy'] = False
self.flag['train'] = False
elif key == 'r':
self.Reset()
elif key == 'p':
self.flag['replay'] = True
self.frame = 0
print "START replay"
self.flag['play'] = False
self.flag['greedy'] = False
self.flag['train'] = False
elif key == 't':
self.flag['trajectory'] = not self.flag['trajectory']
print "traj : ", self.flag['trajectory']
elif key == 'n':
print "One Step"
self.flag['play'] = True
self.flag['greedy'] = False
self.flag['step'] = True
elif key == 'g':
print "Policy : Greedy"
self.flag['play'] = True
self.flag['greedy'] = True
def reshape(self, w, h):
glViewport(0, 0, w, h)
glMatrixMode(GL_PROJECTION)
glLoadIdentity()
glOrtho(-self.WIDTH / 2, self.WIDTH / 2, -self.HEIGHT / 2,
self.HEIGHT / 2, -30, 30)
glMatrixMode(GL_MODELVIEW)
glLoadIdentity()
def display(self):
glClearColor(0.9, 0.9, 0.9, 0.0)
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)
glMatrixMode(GL_MODELVIEW)
glLoadIdentity()
gluLookAt(0, 0, 30, 0, 0, 0, 0, 1, 0)
glPushMatrix()
if self.flag['replay'] and self.Scenario.record_size != 0:
self.Scenario.render_record(self.frame)
else:
self.Scenario.render(depth=self.flag['depth'],
trajectory=self.flag['trajectory'])
glPopMatrix()
glutSwapBuffers()
