def test_api():
env = sumo_rl.env(net_file='nets/4x4-Lucas/4x4.net.xml',
route_file='nets/4x4-Lucas/4x4c1c2c1c2.rou.xml',
out_csv_name='outputs/4x4grid/test',
use_gui=False,
num_seconds=80000)
api_test(env)
env.close()
def ingolstadt1(parallel=True, **kwargs):
"""
Number of agents: 1
Number of actions: 3
"""
kwargs.update({
'net_file': PATH + '/../nets/RESCO/ingolstadt1/ingolstadt1.net.xml',
'route_file': PATH + '/../nets/RESCO/ingolstadt1/ingolstadt1.rou.xml',
'begin_time': 57600,
'num_seconds': 61200
})
if parallel:
return parallel_env(**kwargs)
else:
return env(**kwargs)
def cologne8(parallel=True, **kwargs):
"""
Number of agents: 8
Number of actions: variable
"""
kwargs.update({
'net_file': PATH + '/../nets/RESCO/cologne8/cologne8.net.xml',
'route_file': PATH + '/../nets/RESCO/cologne8/cologne8.rou.xml',
'begin_time': 25200,
'num_seconds': 28800
})
if parallel:
return parallel_env(**kwargs)
else:
return env(**kwargs)
def grid4x4(parallel=True, **kwargs):
"""
Number of agents = 16
Number of actions = 4
Agents have the same obsevation and action space
"""
kwargs.update({
'net_file': PATH + '/../nets/RESCO/grid4x4/grid4x4.net.xml',
'route_file': PATH + '/../nets/RESCO/grid4x4/grid4x4_1.rou.xml',
'num_seconds': 3600
})
if parallel:
return parallel_env(**kwargs)
else:
return env(**kwargs)
def cologne3(parallel=True, **kwargs):
"""
Number of agents: 3
Number of actions: 2 agents with 4 actions and 1 agent with 3 actions
2 agents have the same obsevation and action space and 1 has different spaces
"""
kwargs.update({
'net_file': PATH + '/../nets/RESCO/cologne3/cologne3.net.xml',
'route_file': PATH + '/../nets/RESCO/cologne3/cologne3.rou.xml',
'begin_time': 25200,
'num_seconds': 28800
})
if parallel:
return parallel_env(**kwargs)
else:
return env(**kwargs)
import traci
import sumo_rl
from sumo_rl.agents import QLAgent
from sumo_rl.exploration import EpsilonGreedy
if __name__ == '__main__':
alpha = 0.1
gamma = 0.99
decay = 1
runs = 1
env = sumo_rl.env(net_file='nets/4x4-Lucas/4x4.net.xml',
route_file='nets/4x4-Lucas/4x4c1c2c1c2.rou.xml',
use_gui=False,
min_green=8,
delta_time=5,
num_seconds=80000)
for run in range(1, runs + 1):
env.reset()
initial_states = {ts: env.observe(ts) for ts in env.agents}
ql_agents = {
ts:
QLAgent(starting_state=env.unwrapped.env.encode(
initial_states[ts], ts),
state_space=env.observation_space(ts),
action_space=env.action_space(ts),
alpha=alpha,
gamma=gamma,
exploration_strategy=EpsilonGreedy(initial_epsilon=0.05,
from ray.rllib.agents.a3c.a3c import A3CTrainer
from ray.rllib.agents.a3c.a3c_tf_policy import A3CTFPolicy
from ray.rllib.env import PettingZooEnv
from ray.tune.registry import register_env
from gym import spaces
import numpy as np
import sumo_rl
import traci
if __name__ == '__main__':
ray.init()
register_env("4x4grid", lambda _: PettingZooEnv(sumo_rl.env(net_file='nets/4x4-Lucas/4x4.net.xml',
route_file='nets/4x4-Lucas/4x4c1c2c1c2.rou.xml',
out_csv_name='outputs/4x4grid/a3c',
use_gui=False,
num_seconds=80000)))
trainer = A3CTrainer(env="4x4grid", config={
"multiagent": {
"policies": {
'0': (A3CTFPolicy, spaces.Box(low=np.zeros(11), high=np.ones(11)), spaces.Discrete(2), {})
},
"policy_mapping_fn": (lambda id: '0') # Traffic lights are always controlled by this policy
},
"lr": 0.001,
"no_done_at_end": True
})
while True:
print(trainer.train()) # distributed training step