Brooklyn_DeepRL

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Brooklyn_DeepRL

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Urban block renewal maximizing outdoor thermal comfort using deep reinforcement learning methods.

Brooklyn_DeepRL 최신버젼 다운로드

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# Brooklyn_DeepRL
Urban block renewal maximizing outdoor thermal comfort using deep reinforcement learning methods.

## Action space
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.

```python
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))
```

## Reward function
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:

```python
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 = False
```

### Terminal state condition
The terminal state is reached after 25 buildings are placed or if the built area ratio reaches 90%.

## Training

 <img src="images/brooklyn_a2c_comp.gif" width='700'/>


 <img src="images/Reward_A2C.png" width='700'/>

## References
- [Asynchronous Methods for Deep Reinforcement Learning - Mnih et al. (2016)](https://arxiv.org/abs/1602.01783).
- [Efficient Entropy for Policy Gradient with Multidimensional Action Space - Zhang et al. (2018)](https://arxiv.org/abs/1806.00589).

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