A Smart City Env designed for Reinforcement Learning
A Smart City Env designed for Reinforcement Learning
seed = # given seed env = gym.make("SmartCity-v1337") env.seed(seed)
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| Tool | What it does |
|---|---|
| lights | numpy array of size #light. Each light is either 0 (off) or 1 (on) |
| heaters | numpy array of size #heater. Each heater is defined by its temperature |
| energies_cost | cost of each quantity. This cost is defined for each point of each energy |
| energies_amount | available amount for energy |
| needed_energy | the total amount of energy needed. If you don't send enough energy, an exception is raised. During Hackathon submission, it will be an elimination criteria. If you buy more energy that needed, it is stored indefinitely s |
| light_interraction | a matrix of shape (#people, #lights). If there's a 1 at [i, j], the citizen i is connected to light light |
| heaters_interraction | same as lights but for heaters |
| energy_pollution | pollution factor for each type of enegy (carbon, nuclear, renewable) |
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