def main(method):
args = built_parser(method=method)
env = gym.make(args.env_name)
state_dim = env.observation_space.shape
action_dim = env.action_space.shape[0]
args.state_dim = state_dim
args.action_dim = action_dim
action_high = env.action_space.high
action_low = env.action_space.low
args.action_high = action_high.tolist()
args.action_low = action_low.tolist()
args.seed = np.random.randint(0, 30)
args.init_time = time.time()
if args.alpha == 'auto' and args.target_entropy == 'auto':
delta_a = np.array(args.action_high, dtype=np.float32) - np.array(
args.action_low, dtype=np.float32)
args.target_entropy = -1 * args.action_dim #+ sum(np.log(delta_a/2))
Q_net1 = QNet(args)
Q_net1.train()
Q_net1.share_memory()
Q_net1_target = QNet(args)
Q_net1_target.train()
Q_net1_target.share_memory()
Q_net2 = QNet(args)
Q_net2.train()
Q_net2.share_memory()
Q_net2_target = QNet(args)
Q_net2_target.train()
Q_net2_target.share_memory()
actor1 = PolicyNet(args)
actor1.train()
actor1.share_memory()
actor1_target = PolicyNet(args)
actor1_target.train()
actor1_target.share_memory()
actor2 = PolicyNet(args)
actor2.train()
actor2.share_memory()
actor2_target = PolicyNet(args)
actor2_target.train()
actor2_target.share_memory()
Q_net1_target.load_state_dict(Q_net1.state_dict())
Q_net2_target.load_state_dict(Q_net2.state_dict())
actor1_target.load_state_dict(actor1.state_dict())
actor2_target.load_state_dict(actor2.state_dict())
Q_net1_optimizer = my_optim.SharedAdam(Q_net1.parameters(),
lr=args.critic_lr)
Q_net1_optimizer.share_memory()
Q_net2_optimizer = my_optim.SharedAdam(Q_net2.parameters(),
lr=args.critic_lr)
Q_net2_optimizer.share_memory()
actor1_optimizer = my_optim.SharedAdam(actor1.parameters(),
lr=args.actor_lr)
actor1_optimizer.share_memory()
actor2_optimizer = my_optim.SharedAdam(actor2.parameters(),
lr=args.actor_lr)
actor2_optimizer.share_memory()
log_alpha = torch.zeros(1, dtype=torch.float32, requires_grad=True)
log_alpha.share_memory_()
alpha_optimizer = my_optim.SharedAdam([log_alpha], lr=args.alpha_lr)
alpha_optimizer.share_memory()
share_net = [
Q_net1, Q_net1_target, Q_net2, Q_net2_target, actor1, actor1_target,
actor2, actor2_target, log_alpha
]
share_optimizer = [
Q_net1_optimizer, Q_net2_optimizer, actor1_optimizer, actor2_optimizer,
alpha_optimizer
]
experience_in_queue = []
experience_out_queue = []
for i in range(args.num_buffers):
experience_in_queue.append(Queue(maxsize=10))
experience_out_queue.append(Queue(maxsize=10))
shared_queue = [experience_in_queue, experience_out_queue]
step_counter = mp.Value('i', 0)
stop_sign = mp.Value('i', 0)
iteration_counter = mp.Value('i', 0)
shared_value = [step_counter, stop_sign, iteration_counter]
lock = mp.Lock()
procs = []
if args.code_model == "train":
for i in range(args.num_actors):
procs.append(
Process(target=actor_agent,
args=(args, shared_queue, shared_value,
[actor1, Q_net1], lock, i)))
for i in range(args.num_buffers):
procs.append(
Process(target=buffer,
args=(args, shared_queue, shared_value, i)))
procs.append(
Process(target=evaluate_agent,
args=(args, shared_value, share_net)))
for i in range(args.num_learners):
#device = torch.device("cuda")
device = torch.device("cpu")
procs.append(
Process(target=leaner_agent,
args=(args, shared_queue, shared_value, share_net,
share_optimizer, device, lock, i)))
elif args.code_model == "simu":
procs.append(Process(target=simu_agent, args=(args, shared_value)))
for p in procs:
p.start()
for p in procs:
p.join()
def main(method):
params = {
'obs_size': (160, 100), # screen size of cv2 window
'dt': 0.025, # time interval between two frames
'ego_vehicle_filter':
'vehicle.lincoln*', # filter for defining ego vehicle
'port': 2000, # connection port
'task_mode':
'Straight', # mode of the task, [random, roundabout (only for Town03)]
'code_mode': 'train',
'max_time_episode': 100, # maximum timesteps per episode
'desired_speed': 15, # desired speed (m/s)
'max_ego_spawn_times': 100, # maximum times to spawn ego vehicle
}
args = built_parser(method=method)
env = gym.make(args.env_name, params=params)
state_dim = env.state_space.shape
action_dim = env.action_space.shape[0]
args.state_dim = state_dim
args.action_dim = action_dim
action_high = env.action_space.high
action_low = env.action_space.low
args.action_high = action_high.tolist()
args.action_low = action_low.tolist()
args.seed = np.random.randint(0, 30)
args.init_time = time.time()
num_cpu = mp.cpu_count()
print(state_dim, action_dim, action_high, num_cpu)
if args.alpha == 'auto' and args.target_entropy == 'auto':
delta_a = np.array(args.action_high, dtype=np.float32) - np.array(
args.action_low, dtype=np.float32)
args.target_entropy = -1 * args.action_dim # + sum(np.log(delta_a/2))
Q_net1 = QNet(args)
Q_net1.train()
Q_net1.share_memory()
Q_net1_target = QNet(args)
Q_net1_target.train()
Q_net1_target.share_memory()
Q_net2 = QNet(args)
Q_net2.train()
Q_net2.share_memory()
Q_net2_target = QNet(args)
Q_net2_target.train()
Q_net2_target.share_memory()
actor1 = PolicyNet(args)
print("Network inited")
if args.code_model == "eval":
actor1.load_state_dict(
torch.load('./' + args.env_name + '/method_' + str(args.method) +
'/model/policy_' + str(args.max_train) + '.pkl'))
actor1.train()
actor1.share_memory()
actor1_target = PolicyNet(args)
actor1_target.train()
actor1_target.share_memory()
actor2 = PolicyNet(args)
actor2.train()
actor2.share_memory()
actor2_target = PolicyNet(args)
actor2_target.train()
actor2_target.share_memory()
print("Network set")
Q_net1_target.load_state_dict(Q_net1.state_dict())
Q_net2_target.load_state_dict(Q_net2.state_dict())
actor1_target.load_state_dict(actor1.state_dict())
actor2_target.load_state_dict(actor2.state_dict())
print("Network loaded!")
Q_net1_optimizer = my_optim.SharedAdam(Q_net1.parameters(),
lr=args.critic_lr)
Q_net1_optimizer.share_memory()
Q_net2_optimizer = my_optim.SharedAdam(Q_net2.parameters(),
lr=args.critic_lr)
Q_net2_optimizer.share_memory()
actor1_optimizer = my_optim.SharedAdam(actor1.parameters(),
lr=args.actor_lr)
actor1_optimizer.share_memory()
actor2_optimizer = my_optim.SharedAdam(actor2.parameters(),
lr=args.actor_lr)
actor2_optimizer.share_memory()
log_alpha = torch.zeros(1, dtype=torch.float32, requires_grad=True)
log_alpha.share_memory_()
alpha_optimizer = my_optim.SharedAdam([log_alpha], lr=args.alpha_lr)
alpha_optimizer.share_memory()
print("Optimizer done")
share_net = [
Q_net1, Q_net1_target, Q_net2, Q_net2_target, actor1, actor1_target,
actor2, actor2_target, log_alpha
]
share_optimizer = [
Q_net1_optimizer, Q_net2_optimizer, actor1_optimizer, actor2_optimizer,
alpha_optimizer
]
experience_in_queue = []
experience_out_queue = []
for i in range(args.num_buffers):
experience_in_queue.append(Queue(maxsize=10))
experience_out_queue.append(Queue(maxsize=10))
shared_queue = [experience_in_queue, experience_out_queue]
step_counter = mp.Value('i', 0)
stop_sign = mp.Value('i', 0)
iteration_counter = mp.Value('i', 0)
shared_value = [step_counter, stop_sign, iteration_counter]
lock = mp.Lock()
procs = []
if args.code_model == "train":
for i in range(args.num_learners):
if i % 2 == 0:
device = torch.device("cuda:1")
else:
device = torch.device("cuda:0")
# device = torch.device("cpu")
procs.append(
Process(target=leaner_agent,
args=(args, shared_queue, shared_value, share_net,
share_optimizer, device, lock, i)))
for i in range(args.num_actors):
procs.append(
Process(target=actor_agent,
args=(args, shared_queue, shared_value,
[actor1, Q_net1], lock, i)))
for i in range(args.num_buffers):
procs.append(
Process(target=buffer,
args=(args, shared_queue, shared_value, i)))
procs.append(
Process(target=evaluate_agent,
args=(args, shared_value, share_net)))
elif args.code_model == "simu":
procs.append(Process(target=simu_agent, args=(args, shared_value)))
for p in procs:
p.start()
for p in procs:
p.join()
