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worker.py
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worker.py
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import copy
import os
import imageio
import numpy as np
import torch
from env import Env
from parameter import *
class Worker:
def __init__(self, meta_agent_id, policy_net, q_net, global_step, device='cuda', greedy=False, save_image=True):
self.device = device
self.greedy = greedy
self.metaAgentID = meta_agent_id
self.global_step = global_step
self.node_padding_size = NODE_PADDING_SIZE
self.k_size = K_SIZE
self.save_image = save_image
self.env = Env(map_index=self.global_step, k_size=self.k_size, plot=save_image)
self.local_policy_net = policy_net
self.local_q_net = q_net
self.current_node_index = 0
self.travel_dist = 0
self.robot_position = self.env.start_position
self.episode_buffer = []
self.perf_metrics = dict()
for i in range(15):
self.episode_buffer.append([])
def get_observations(self):
# get observations
node_coords = copy.deepcopy(self.env.node_coords)
graph = copy.deepcopy(self.env.graph)
node_utility = copy.deepcopy(self.env.node_utility)
indicator = copy.deepcopy(self.env.indicator)
direction_vector = copy.deepcopy(self.env.direction_vector)
# normalize observations
node_coords = node_coords / 640
node_utility = node_utility / 50
n_nodes = node_coords.shape[0]
node_utility_inputs = node_utility.reshape(n_nodes, 1)
direction_nums = direction_vector.shape[0]
direction_vector_inputs = direction_vector.reshape(direction_nums, 3)
direction_vector_inputs[:, 2] /= 80
node_inputs = np.concatenate((node_coords, node_utility_inputs, indicator, direction_vector_inputs), axis=1)
node_inputs = torch.FloatTensor(node_inputs).unsqueeze(0).to(self.device) # (1, node_padding_size+1, 3)
assert node_coords.shape[0] < self.node_padding_size
padding = torch.nn.ZeroPad2d((0, 0, 0, self.node_padding_size - node_coords.shape[0]))
node_inputs = padding(node_inputs)
# calculate a mask to padded nodes
node_padding_mask = torch.zeros((1, 1, node_coords.shape[0]), dtype=torch.int64).to(self.device)
node_padding = torch.ones((1, 1, self.node_padding_size - node_coords.shape[0]), dtype=torch.int64).to(
self.device)
node_padding_mask = torch.cat((node_padding_mask, node_padding), dim=-1)
# get the node index of the current robot position
current_node_index = self.env.find_index_from_coords(self.robot_position)
current_index = torch.tensor([current_node_index]).unsqueeze(0).unsqueeze(0).to(self.device) # (1,1,1)
# prepare the adjacent list as padded edge inputs and the adjacent matrix as the edge mask
graph = list(graph.values())
edge_inputs = []
for node in graph:
node_edges = list(map(int, node))
edge_inputs.append(node_edges)
adjacent_matrix = self.calculate_edge_mask(edge_inputs)
edge_mask = torch.from_numpy(adjacent_matrix).float().unsqueeze(0).to(self.device)
assert len(edge_inputs) < self.node_padding_size
padding = torch.nn.ConstantPad2d(
(0, self.node_padding_size - len(edge_inputs), 0, self.node_padding_size - len(edge_inputs)), 1)
edge_mask = padding(edge_mask)
edge = edge_inputs[current_index]
while len(edge) < self.k_size:
edge.append(0)
edge_inputs = torch.tensor(edge).unsqueeze(0).unsqueeze(0).to(self.device) # (1, 1, k_size)
# calculate a mask for the padded edges (denoted by 0)
edge_padding_mask = torch.zeros((1, 1, K_SIZE), dtype=torch.int64).to(self.device)
one = torch.ones_like(edge_padding_mask, dtype=torch.int64).to(self.device)
edge_padding_mask = torch.where(edge_inputs == 0, one, edge_padding_mask)
observations = node_inputs, edge_inputs, current_index, node_padding_mask, edge_padding_mask, edge_mask
return observations
def select_node(self, observations):
node_inputs, edge_inputs, current_index, node_padding_mask, edge_padding_mask, edge_mask = observations
with torch.no_grad():
logp_list = self.local_policy_net(node_inputs, edge_inputs, current_index, node_padding_mask,
edge_padding_mask, edge_mask)
if self.greedy:
action_index = torch.argmax(logp_list, dim=1).long()
else:
action_index = torch.multinomial(logp_list.exp(), 1).long().squeeze(1)
next_node_index = edge_inputs[0, 0, action_index.item()]
next_position = self.env.node_coords[next_node_index]
return next_position, action_index
def save_observations(self, observations):
node_inputs, edge_inputs, current_index, node_padding_mask, edge_padding_mask, edge_mask = observations
self.episode_buffer[0] += copy.deepcopy(node_inputs)
self.episode_buffer[1] += copy.deepcopy(edge_inputs)
self.episode_buffer[2] += copy.deepcopy(current_index)
self.episode_buffer[3] += copy.deepcopy(node_padding_mask)
self.episode_buffer[4] += copy.deepcopy(edge_padding_mask)
self.episode_buffer[5] += copy.deepcopy(edge_mask)
def save_action(self, action_index):
self.episode_buffer[6] += action_index.unsqueeze(0).unsqueeze(0)
def save_reward_done(self, reward, done):
self.episode_buffer[7] += copy.deepcopy(torch.FloatTensor([[[reward]]]).to(self.device))
self.episode_buffer[8] += copy.deepcopy(torch.tensor([[[(int(done))]]]).to(self.device))
def save_next_observations(self, observations):
node_inputs, edge_inputs, current_index, node_padding_mask, edge_padding_mask, edge_mask = observations
self.episode_buffer[9] += copy.deepcopy(node_inputs)
self.episode_buffer[10] += copy.deepcopy(edge_inputs)
self.episode_buffer[11] += copy.deepcopy(current_index)
self.episode_buffer[12] += copy.deepcopy(node_padding_mask)
self.episode_buffer[13] += copy.deepcopy(edge_padding_mask)
self.episode_buffer[14] += copy.deepcopy(edge_mask)
def run_episode(self, curr_episode):
done = False
observations = self.get_observations()
for i in range(128):
self.save_observations(observations)
next_position, action_index = self.select_node(observations)
self.save_action(action_index)
reward, done, self.robot_position, self.travel_dist = self.env.step(self.robot_position, next_position, self.travel_dist)
self.save_reward_done(reward, done)
observations = self.get_observations()
self.save_next_observations(observations)
if self.save_image:
if not os.path.exists(gifs_path):
os.makedirs(gifs_path)
self.env.plot_env(self.global_step, gifs_path, i, self.travel_dist)
if done:
break
self.perf_metrics['travel_dist'] = self.travel_dist
self.perf_metrics['explored_rate'] = self.env.explored_rate
self.perf_metrics['success_rate'] = done
if self.save_image:
path = gifs_path
self.make_gif(path, curr_episode)
def work(self, currEpisode):
self.run_episode(currEpisode)
def calculate_edge_mask(self, edge_inputs):
size = len(edge_inputs)
bias_matrix = np.ones((size, size))
for i in range(size):
for j in range(size):
if j in edge_inputs[i]:
bias_matrix[i][j] = 0
return bias_matrix
def make_gif(self, path, n):
with imageio.get_writer('{}/{}_explored_rate_{:.4g}.gif'.format(path, n, self.env.explored_rate), mode='I', duration=0.5) as writer:
for frame in self.env.frame_files:
image = imageio.imread(frame)
writer.append_data(image)
print('gif complete\n')
for filename in self.env.frame_files[:-1]:
os.remove(filename)