def __init__(self, num_inputs, action_space, param, action_bound):
self.action_bound = action_bound
self.alpha = param['alpha']
self.gamma = param['gamma']
self.tau = param['tau']
self.target_update_interval = param['target_update_interval']
self.automatic_entropy_tuning = param['automatic_entropy_tuning']
self.lr = param['lr']
self.device = torch.device("cuda" if param['cuda'] else "cpu")
self.critic = QNetwork(num_inputs, action_space).to(device=self.device)
self.critic_optim = Adam(self.critic.parameters(), lr=self.lr)
self.critic_target = QNetwork(num_inputs, action_space).to(self.device)
hard_update(self.critic_target, self.critic)
# Target Entropy = -dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
if self.automatic_entropy_tuning is True:
self.target_entropy = -torch.prod(
torch.Tensor(action_space).to(self.device)).item()
self.log_alpha = torch.zeros(1,
requires_grad=True,
device=self.device)
self.alpha_optim = Adam([self.log_alpha], lr=param['lr'])
self.policy = CNNPolicy(num_inputs, action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=param['lr'])
def run():
# Set parameters of env
LASER_HIST = 3
NUM_ENV = 1 # the number of agents in the environment
OBS_SIZE = 512 # number of leaserbeam
action_bound = [[0, -1], [1, 1]] # the limitation of velocity
# Set env and agent policy
env = StageWorld(
OBS_SIZE, index=0, num_env=NUM_ENV
) #index is useful for parallel programming, 0 is for the first agent
trained_model_file = os.path.dirname(__file__) + '/policy/stage2.pth'
policy = CNNPolicy(frames=LASER_HIST, action_space=2)
policy.cpu() # policy.cuda() for gpu
state_dict = torch.load(
trained_model_file, map_location=torch.device(
'cpu')) #torch.load(trained_model_file) for gpu
policy.load_state_dict(state_dict)
rospy.init_node('rl_collision_avoidance_tb3', anonymous=False)
print(
'==================================\nrl_collision_avoidance node started'
)
nn_tb3 = NN_tb3(env,
policy,
action_bound,
OBS_SIZE,
index=0,
num_env=NUM_ENV)
rospy.on_shutdown(nn_tb3.on_shutdown)
rospy.spin()
logger_cal.setLevel(logging.INFO)
cal_f_handler = logging.FileHandler(cal_file, mode='a')
file_handler.setLevel(logging.INFO)
logger_cal.addHandler(cal_f_handler)
# logger.info('rosport: %d robotIndex: %d rank:%d' %(arg_list.rosports[rank],arg_list.robotIds[rank],rank))
reward = None
action_bound = [[0, -1], [1, 1]]
# torch.manual_seed(1)
# np.random.seed(1)
if rank == 0:
policy_path = policydir
# policy_path = 'policy'
# policy = MLPPolicy(obs_size, act_size)
policy = CNNPolicy(frames=LASER_HIST, action_space=2)
policy.cuda()
opt = Adam(policy.parameters(), lr=LEARNING_RATE)
mse = nn.MSELoss()
if not os.path.exists(policy_path):
os.makedirs(policy_path)
file = policy_path + '/Stage1_%d' % ID_EP
if os.path.exists(file):
logger.info('####################################')
logger.info('############Loading Model###########')
logger.info('####################################')
state_dict = torch.load(file)
policy.load_state_dict(state_dict)
else:
class SAC(object):
def __init__(self, num_inputs, action_space, param, action_bound):
self.action_bound = action_bound
self.alpha = param['alpha']
self.gamma = param['gamma']
self.tau = param['tau']
self.target_update_interval = param['target_update_interval']
self.automatic_entropy_tuning = param['automatic_entropy_tuning']
self.lr = param['lr']
self.device = torch.device("cuda" if param['cuda'] else "cpu")
self.critic = QNetwork(num_inputs, action_space).to(device=self.device)
self.critic_optim = Adam(self.critic.parameters(), lr=self.lr)
self.critic_target = QNetwork(num_inputs, action_space).to(self.device)
hard_update(self.critic_target, self.critic)
# Target Entropy = -dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper
if self.automatic_entropy_tuning is True:
self.target_entropy = -torch.prod(
torch.Tensor(action_space).to(self.device)).item()
self.log_alpha = torch.zeros(1,
requires_grad=True,
device=self.device)
self.alpha_optim = Adam([self.log_alpha], lr=param['lr'])
self.policy = CNNPolicy(num_inputs, action_space).to(self.device)
self.policy_optim = Adam(self.policy.parameters(), lr=param['lr'])
def select_action(self, state, evaluate=False):
#state = torch.FloatTensor(state).to(self.device).unsqueeze(0)
obs_stack, s_list, speed = state
obs_stack = Variable(torch.from_numpy(obs_stack)).float().cuda()
goal_list = Variable(torch.from_numpy(goal_list)).float().cuda()
speed = Variable(torch.from_numpy(speed)).float().cuda()
if evaluate is False:
action, _, _ = self.policy.sample(obs_stack, goal_list, speed)
else:
_, _, action = self.policy.sample(obs_stack, goal_list, speed)
a = a.data.cpu().numpy()
scaled_action = np.clip(a,
a_min=slef.action_bound[0],
a_max=self.action_bound[1])
return scaled_action
def update_parameters(self, memory, updates):
state_batch, action_batch, reward_batch, next_state_batch, done_batch = memory
obss, goals, speeds = state_batch
obss_, goals_, speeds_ = next_state_batch
# state_batch = torch.FloatTensor(state_batch).to(self.device)
# next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
obss = torch.FloatTensor(obss).to(self.device)
goals = torch.FloatTensor(goals).to(self.device)
speeds = torch.FloatTensor(speeds).to(self.device)
obss_ = torch.FloatTensor(obss_).to(self.device)
goals_ = torch.FloatTensor(goals_).to(self.device)
speeds_ = torch.FloatTensor(speeds_).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(
self.device).unsqueeze(1)
done_batch = torch.FloatTensor(done_batch).to(self.device).unsqueeze(1)
with torch.no_grad():
next_state_action, next_state_log_pi, _ = self.policy.sample(
obss_, goals_, speeds_)
qf1_next_target, qf2_next_target = self.critic_target(
obss_, goals_, speeds_, next_state_action)
min_qf_next_target = torch.min(
qf1_next_target,
qf2_next_target) - self.alpha * next_state_log_pi
next_q_value = reward_batch + done_batch * self.gamma * (
min_qf_next_target)
qf1, qf2 = self.critic(
obss, goals, speeds, action_batch
) # Two Q-functions to mitigate positive bias in the policy improvement step
qf1_loss = F.mse_loss(qf1, next_q_value)
qf2_loss = F.mse_loss(qf2, next_q_value)
pi, log_pi, _ = self.policy.sample(obss, goals, speeds)
qf1_pi, qf2_pi = self.critic(obss, goals, speeds, pi)
min_qf_pi = torch.min(qf1_pi, qf2_pi)
policy_loss = ((self.alpha * log_pi) - min_qf_pi).mean()
self.critic_optim.zero_grad()
qf1_loss.backward()
self.critic_optim.step()
self.critic_optim.zero_grad()
qf2_loss.backward()
self.critic_optim.step()
self.policy_optim.zero_grad()
policy_loss.backward()
self.policy_optim.step()
if self.automatic_entropy_tuning:
alpha_loss = -(self.log_alpha *
(log_pi + self.target_entropy).detach()).mean()
self.alpha_optim.zero_grad()
alpha_loss.backward()
self.alpha_optim.step()
self.alpha = self.log_alpha.exp()
alpha_tlogs = self.alpha.clone() # For TensorboardX logs
else:
alpha_loss = torch.tensor(0.).to(self.device)
alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs
if updates % self.target_update_interval == 0:
soft_update(self.critic_target, self.critic, self.tau)
return qf1_loss.item(), qf2_loss.item(), policy_loss.item(
), alpha_loss.item(), alpha_tlogs.item()
def get_policy_state_dict(self):
return self.policy.state_dict()
def load_policy_state_dict(self, statedict):
self.policy.load_state_dict(statedict)
def load_model(self, actor_path, critic_path):
if actor_path is not None:
self.policy.load_state_dict(torch.load(actor_path))
if critic_path is not None:
self.critic.load_state_dict(torch.load(critic_path))
def save_model(self,
env_name,
suffix="",
actor_path=None,
critic_path=None):
if not os.path.exists('models/'):
os.makedirs('models/')
if actor_path is None:
actor_path = "models/sac_actor_{}_{}".format(env_name, suffix)
if critic_path is None:
critic_path = "models/sac_critic_{}_{}".format(env_name, suffix)
print('Saving models to {} and {}'.format(actor_path, critic_path))
torch.save(self.policy.state_dict(), actor_path)
torch.save(self.critic.state_dict(), critic_path)
# Set parameters of env
LASER_HIST = 3
NUM_ENV = 10 # the number of agents in the environment
OBS_SIZE = 512 # number of leaserbeam
action_bound = [[0, -1], [1, 1]] # the limitation of velocity
# Set env and agent policy
env = StageWorld(
OBS_SIZE, index=0, num_env=NUM_ENV
) #index is useful for parallel programming, 0 is for the first agent
env.reset_world()
env.reset_pose()
trained_model_file = 'policy/stage2.pth'
policy = CNNPolicy(frames=LASER_HIST, action_space=2)
policy.cpu() # policy.cuda() for gpu
state_dict = torch.load(
trained_model_file,
map_location=torch.device('cpu')) #torch.load(trained_model_file) for gpu
policy.load_state_dict(state_dict)
# Set fake obstacles by fake lidar info
obstacles_location = []
for i in range(NUM_ENV - 1):
obstacles_location.append(np.zeros(512))
obstacles_location[i][10 * i:10 * i + 10] = 0.1 * i + 0.05
# Configure agent as host
obs = env.get_laser_observation()
for i in range(NUM_ENV - 1):
