def main(config):
reward_rule = VRPReward(**config['reward_params'])
log("Using Network Device {}".format(network_device))
M = make_environ(reward_rule=reward_rule,
device=network_device,
verbose=False,
**config['env_params'])
# M.print_events()
config['use_neptune'] = config['use_neptune'] and config['mode'] == 'train'
log_name = str(datetime.now()).replace(" ", "-").replace(":", "-").replace(".", "-")
config['agent_params']['log_path'] = os.path.join(config['agent_params']['save_dir'], log_name)
os.makedirs(config['agent_params']['log_path'], exist_ok=True)
if config['use_neptune']:
import neptune
nep_args = config['neptune_params']
token = os.environ['NEPTUNE_API_TOKEN']
proj_name = nep_args['proj_name']
neptune.init(project_qualified_name=proj_name, api_token=token)
neptune.create_experiment(params=make_params_dict(config), upload_source_files=[args.config])
agent = DQN_Agent(M, config['agent_params'], network_device, memory_device, config['use_neptune'])
if config['mode'] == 'train':
agent.train()
def main(config):
os.makedirs(animation_path, exist_ok=True)
reward_rule = VRPReward(**config['reward_params'])
log("Using Network Device {}".format('cpu'))
M = make_environ(reward_rule=reward_rule,
device='cpu',
verbose=False,
**config['env_params'])
# M.print_events()
A = UniRandAgent(M)
agent = DQN_Agent(M, config['agent_params'], 'cpu', 'cpu',
config['use_neptune'])
agent.load(model_path)
vis_map(M,
M.vec,
background=False,
margin=1.3,
info=img_info,
im_name='./dummy.jpg')
img_shape = get_size()
M.assign_auto_agent(A)
print(img_shape)
M.reset()
video_name = os.path.join(animation_path,
"{}.mp4".format(img_info.replace(' ', '')))
gif_name = os.path.join(animation_path,
"{}.gif".format(img_info.replace(' ', '')))
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
video = cv2.VideoWriter(video_name, fourcc, 8,
(img_shape[1], img_shape[0]))
with imageio.get_writer(gif_name, mode='I') as gif_writer:
for t in trange(T):
im_name = os.path.join(animation_path, "{}.jpg".format(t))
action = agent.act(M.vec_flatten, 0, action_rng)
M.feedback_step(action)
# M.step_auto()
vis_map(M,
M.vec,
background=False,
margin=1.3,
info=img_info,
im_name=im_name)
im_vec = cv2.imread(im_name)
video.write(im_vec)
gif_writer.append_data(im_vec)
os.remove(im_name)
cv2.destroyAllWindows()
video.release()
def __init__(self,
queue_reward=0,
wait_reward=0,
opr_reward=0,
time_penalty_power=1,
efficiency_reward=0,
invalid_route_reward=0):
self.queue_reward = queue_reward
self.wait_reward = wait_reward
self.opr_reward = opr_reward
self.time_penalty_power = time_penalty_power
self.efficiency_reward = efficiency_reward
self.invalid_route_reward = invalid_route_reward
log("Initialize reward function | QueueCost:{} | WaitCost:{} | OprCost:{} | TimePenaltyPower:{} | BusEfficiency:{} | InvalidRouteCost:{}"
.format(self.queue_reward, self.wait_reward, self.opr_reward,
self.time_penalty_power, self.efficiency_reward,
self.invalid_route_reward))
def __init__(self, capacity, device, state_size, n_action, hist_len):
self.capacity = capacity
self.device = device
log("Initializing Episode Memory | Capacity {:.3g} | Requesting {:.4g}GB"
.format(capacity,
8 * capacity * (2 * state_size + n_action + 1) / (2**30)))
self.states = torch.zeros([capacity] + [state_size]).to(self.device)
self.actions = torch.zeros([capacity] + [n_action],
dtype=torch.long).to(self.device)
self.rewards = torch.zeros([capacity]).to(self.device)
self.next_states = torch.zeros([capacity] + [state_size]).to(
self.device)
self.hist_len = hist_len
self.position = 0
self.size = 0
log("Episode Memory Initialized")
def main(config):
reward_rule = VRPReward(**config['reward_params'])
log("Using Network Device {}".format(network_device))
M = make_environ(reward_rule=reward_rule,
device=network_device,
verbose=False,
**config['env_params'])
# M.print_events()
config['use_neptune'] = config['use_neptune'] and config['mode'] == 'train'
if config['use_neptune']:
nep_args = config['neptune_params']
token = os.environ['NEPTUNE_API_TOKEN']
proj_name = nep_args['proj_name']
neptune.init(project_qualified_name=proj_name, api_token=token)
neptune.create_experiment(params=make_params_dict(config), upload_source_files=[args.config])
agent = DQN_Agent(M, config['agent_params'], network_device, memory_device, config['use_neptune'])
if config['mode'] == 'train':
agent.train()