Urban block renewal maximizing outdoor thermal comfort using deep reinforcement learning methods.
The agent controls the building location through x, y coordinates, the building orientation angle theta, as well as the length, width, and height of the building. This is done through a 6 dimensional discrete action space.
import numpy as np
import torch
# x coordinate
param1_space = torch.from_numpy(np.linspace(start=456, stop=835, num=250))
# y coordinate
param2_space = torch.from_numpy(np.linspace(start=325, stop=886, num=250))
# building orientation angle theta
param3_space = torch.from_numpy(np.linspace(start=0, stop=180, num=250))
# building length
param4_space = torch.from_numpy(np.linspace(start=20, stop=70, num=250))
# building width
param5_space = torch.from_numpy(np.linspace(start=20, stop=70, num=250))
# building height
param6_space = torch.from_numpy(np.linspace(start=10, stop=200, num=250))The agent starts with a +10 reward at each step and receives a penalty of -10 if the placed building happens to be outside the superblock boundary, plus a -5 penalty if it is not inside any of the buildable plots. The agent is also penalized by -2 if one of the buildings placed is inside one of the existing buildings, and -1 for each intersection between buildings. The reward signal is computed at each step in the Grasshopper environment according to the following code:
try:
from ladybug_rhino.grasshopper import all_required_inputs
except ImportError as e:
raise ImportError('\nFailed to import ladybug_rhino:\n\t{}'.format(e))
if all_required_inputs(ghenv.Component):
reward = 0
done = False
boundary_relationList = [list(i) for i in boundary_relation.Branches]
bArea_relationList = [list(i) for i in bArea_relation.Branches]
bInter_relationList = [list(i) for i in bInter_relation.Branches]
reward += 10
for r in boundary_relationList:
if r[0] != 0:
reward -= 10
for b in bArea_relationList:
if 0 in b:
reward += 2
else:
reward -= 5
for i in bInter_relationList:
for r in i:
if r == 0:
reward -= 2
elif r == 1:
reward -= 1
reward += 1
for gen_area in gen_env_areas:
if env_area_start < gen_area < env_area_end:
reward += 1
else:
reward -= 1
if builtarea_ratio >= 0.9:
done = True
if len(bldg_bool) == 0:
done = FalseThe terminal state is reached after 25 buildings are placed or if the built area ratio reaches 90%.
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