def test_api():
env = SumoEnvironment(single_agent=True,
num_seconds=100000,
net_file='nets/single-intersection/single-intersection.net.xml',
route_file='nets/single-intersection/single-intersection.rou.xml')
env.reset()
check_env(env)
env.close()
def run(use_gui=True, runs=1):
out_csv = 'outputs/double/sarsa-double'
env = SumoEnvironment(net_file='nets/double/network.net.xml',
single_agent=False,
route_file='nets/double/flow.rou.xml',
out_csv_name=out_csv,
use_gui=use_gui,
num_seconds=86400,
yellow_time=3,
min_green=5,
max_green=60)
fixed_tl = False
agents = {
ts_id: TrueOnlineSarsaLambda(env.observation_spaces(ts_id),
env.action_spaces(ts_id),
alpha=0.000000001,
gamma=0.95,
epsilon=0.05,
lamb=0.1,
fourier_order=7)
for ts_id in env.ts_ids
}
for run in range(1, runs + 1):
obs = env.reset()
done = {'__all__': False}
if fixed_tl:
while not done['__all__']:
_, _, done, _ = env.step(None)
else:
while not done['__all__']:
actions = {
ts_id: agents[ts_id].act(obs[ts_id])
for ts_id in obs.keys()
}
next_obs, r, done, _ = env.step(action=actions)
for ts_id in next_obs.keys():
agents[ts_id].learn(state=obs[ts_id],
action=actions[ts_id],
reward=r[ts_id],
next_state=next_obs[ts_id],
done=done[ts_id])
obs[ts_id] = next_obs[ts_id]
env.save_csv(out_csv, run)
out_csv = 'outputs/2way-single-intersection/sarsa_lambdavai'
write_route_file('nets/2way-single-intersection/single-intersection-gen.rou.xml', 400000, 100000)
env = SumoEnvironment(net_file='nets/2way-single-intersection/single-intersection.net.xml',
single_agent=True,
route_file=args.route,
out_csv_name=out_csv,
use_gui=args.gui,
num_seconds=args.seconds,
min_green=args.min_green,
max_green=args.max_green,
max_depart_delay=0)
for run in range(1, args.runs+1):
obs = env.reset()
agent = TrueOnlineSarsaLambda(env.observation_space, env.action_space, alpha=args.alpha, gamma=args.gamma, epsilon=args.epsilon, fourier_order=7, lamb=0.9)
done = False
if args.fixed:
while not done:
_, _, done, _ = env.step({})
else:
while not done:
action = agent.act(obs)
next_obs, r, done, _ = env.step(action=action)
agent.learn(state=obs, action=action, reward=r, next_state=next_obs, done=done)
obs = next_obs
out_csv = 'outputs/single-intersection/{}_alpha{}_gamma{}_eps{}_decay{}_reward{}'.format(
experiment_time, args.alpha, args.gamma, args.epsilon, args.decay,
args.reward)
env = SumoEnvironment(
net_file='nets/single-intersection/single-intersection.net.xml',
route_file=args.route,
out_csv_name=out_csv,
use_gui=args.gui,
num_seconds=args.seconds,
min_green=args.min_green,
max_green=args.max_green,
max_depart_delay=0)
for run in range(1, args.runs + 1):
initial_states = env.reset()
ql_agents = {
ts: QLAgent(starting_state=env.encode(initial_states[ts], ts),
state_space=env.observation_space,
action_space=env.action_space,
alpha=args.alpha,
gamma=args.gamma,
exploration_strategy=EpsilonGreedy(
initial_epsilon=args.epsilon,
min_epsilon=args.min_epsilon,
decay=args.decay))
for ts in env.ts_ids
}
done = {'__all__': False}
infos = []