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ATSPEnv.py
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from dataclasses import dataclass
import torch
from ATSProblemDef import get_random_problems
@dataclass
class Reset_State:
problems: torch.Tensor
# shape: (batch, node, node)
@dataclass
class Step_State:
BATCH_IDX: torch.Tensor
POMO_IDX: torch.Tensor
# shape: (batch, pomo)
current_node: torch.Tensor = None
# shape: (batch, pomo)
ninf_mask: torch.Tensor = None
# shape: (batch, pomo, node)
class ATSPEnv:
def __init__(self, **env_params):
# Const @INIT
####################################
self.env_params = env_params
self.node_cnt = env_params['node_cnt']
self.pomo_size = env_params['pomo_size']
# Const @Load_Problem
####################################
self.batch_size = None
self.BATCH_IDX = None
self.POMO_IDX = None
# IDX.shape: (batch, pomo)
self.problems = None
# shape: (batch, node, node)
# Dynamic
####################################
self.selected_count = None
self.current_node = None
# shape: (batch, pomo)
self.selected_node_list = None
# shape: (batch, pomo, 0~)
# STEP-State
####################################
self.step_state = None
def load_problems(self, batch_size):
self.batch_size = batch_size
self.BATCH_IDX = torch.arange(self.batch_size)[:, None].expand(self.batch_size, self.pomo_size)
self.POMO_IDX = torch.arange(self.pomo_size)[None, :].expand(self.batch_size, self.pomo_size)
problem_gen_params = self.env_params['problem_gen_params']
self.problems = get_random_problems(batch_size, self.node_cnt, problem_gen_params)
# shape: (batch, node, node)
def load_problems_manual(self, problems):
# problems.shape: (batch, node, node)
self.batch_size = problems.size(0)
self.BATCH_IDX = torch.arange(self.batch_size)[:, None].expand(self.batch_size, self.pomo_size)
self.POMO_IDX = torch.arange(self.pomo_size)[None, :].expand(self.batch_size, self.pomo_size)
self.problems, self.scaled_problems_time, self.start, self.end = problems
# shape: (batch, node, node)
def reset(self):
self.selected_count = 0
self.current_node = None
# shape: (batch, pomo)
self.selected_node_list = torch.empty((self.batch_size, self.pomo_size, 0), dtype=torch.long)
# shape: (batch, pomo, 0~)
self._create_step_state()
reward = None
done = False
return Reset_State(problems=self.problems), reward, done
def _create_step_state(self):
self.step_state = Step_State(BATCH_IDX=self.BATCH_IDX, POMO_IDX=self.POMO_IDX)
self.step_state.ninf_mask = torch.zeros((self.batch_size, self.pomo_size, self.node_cnt))
# shape: (batch, pomo, node)
def pre_step(self):
reward = None
done = False
return self.step_state, reward, done
def step(self, node_idx):
# node_idx.shape: (batch, pomo)
self.selected_count += 1
self.current_node = node_idx
# shape: (batch, pomo)
self.selected_node_list = torch.cat((self.selected_node_list, self.current_node[:, :, None]), dim=2)
# shape: (batch, pomo, 0~node)
self._update_step_state()
# returning values
done = (self.selected_count == self.node_cnt)
if done:
reward = -self._get_total_distance() # Note the MINUS Sign ==> We MAXIMIZE reward
# shape: (batch, pomo)
else:
reward = None
return self.step_state, reward, done
def _update_step_state(self):
self.step_state.current_node = self.current_node
# shape: (batch, pomo)
self.step_state.ninf_mask[self.BATCH_IDX, self.POMO_IDX, self.current_node] = float('-inf')
# shape: (batch, pomo, node)
def _get_total_distance(self):
node_from = self.selected_node_list
# shape: (batch, pomo, node)
node_to = self.selected_node_list.roll(dims=2, shifts=-1)
# shape: (batch, pomo, node)
batch_index = self.BATCH_IDX[:, :, None].expand(self.batch_size, self.pomo_size, self.node_cnt)
# shape: (batch, pomo, node)
#####################################################
#define problems_time
#######################################################
# selected_time (shape: batch,pomo,node)
# start, end(shape: node)
# x=torch.cumsum(selected_time, dim =2) // start' = selected_node_list.apply_(lamba x:start[x]) // end' = selected_node_list.apply_(lamba x:end[x])
# time_penalty = (start'>x)element_wise(start'-x) +(x>end')element_wise(x-end')
#####################################################
cost, time, start, end = self.problems
selected_time = time[batch_index,node_from,node_to]
#shape: node
x = torch.cumsum(selected_time, dim=2)
startz = torch.zeros(self.selected_node_list.shape, dtype=torch.float64, device=self.selected_node_list.get_device())
endz = torch.zeros(self.selected_node_list.shape, dtype=torch.float64, device=self.selected_node_list.get_device())
for b,batch in enumerate(startz):
for p, pomo in enumerate(batch):
for i, index in enumerate(pomo):
startz[b][p][i] = start[b][self.selected_node_list[b][p][i].item()].float()
endz[b][p][i] = end[b][self.selected_node_list[b][p][i].item()].float()
# startz = startz.cpu().apply_(lambda x:start[batch_index][int(x)]).to("cuda:0")
# endz = endz.cpu().apply_(lambda x:end[x]).to_device("cuda:0")
time_penalty = torch.mul((torch.lt(startz,x).float()),abs(startz-x))+ torch.mul((torch.lt(x,endz).float()),abs(x-endz))
#######################################################
selected_cost = cost[batch_index, node_from, node_to]
# shape: (batch, pomo, node)
total_distance = selected_cost.sum(2)+time_penalty.sum(2)
# shape: (batch, pomo)
# print()
return total_distance