wingsweihua / gym_cityflow

Adds CityFlow to Gym

Gym environment for CityFlow

Basics

gym_cityflow adds a custom environment from CityFlow following this tutorial.

gym_cityflow/
  README.md
  setup.py
  test.py
  gym_cityflow/
    __init__.py
    envs/
      __init__.py
      fcityflow_1x1.py
      1x1_config/
        config.json
        flow.json
        roadnet.json

Installation

pip install -e .

import gym
import gym_cityflow

env = gym.make('gym_cityflow:CityFlow-1x1-LowTraffic-v0')

CityFlow config.json, flow.json, and roadnet.json are from CityFlow/examples

---

Test Run with DQN

import gym
import gym_cityflow
import numpy as np
from stable_baselines.deepq.policies import MlpPolicy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import DQN

env = gym.make('gym_cityflow:CityFlow-1x1-LowTraffic-v0')
model = DQN(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=25000)

How to add new environments to gym

The explanation below is derived from this cartpole example which is a part of OpenAI s gym.

1. Subclass your environment under gym.Env

import gym
from gym import error, spaces, utils
from gym.utils import seeding

class MyEnv(gym.Env):
    metadata = {'render.modes':['human']}
...

2. override: __init__(self), step(self, action), reset(self), render(self, mode='human'), close(self)

import gym
from gym import error, spaces, utils
from gym.utils import seeding

class MyEnv(gym.Env):
    metadata = {'render.modes':['human']}
    def __init__(self):

    def step(self, action):
        """
        Source: https://gym.openai.com/docs/        

        Args:
            action: action for the agent to take

        Returns:
            tuple of 4 valuess: (state, reward, is_done, info).
            
            state (Object): observation from the agent 
            reward (float): reward
            done (boolean): whether the environment has reached a terminal state
            info (dict): additional information for debugging. MAY NOT be used during evaluation, only 
            used during debugging.
        """

    def reset(self):   
        """
        Retruns:
            state (Object): Initial observation
        """

    def render(self, mode='human'):
        print("hello world")

    def close(self):
        """
        stops the environment
        """