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 = f'outputs/5x5-Raphael/{scenario}_{experiment_time}_alpha{args.alpha}_gamma{args.gamma}_eps{args.epsilon}_decay{args.decay}'
env = SumoEnvironment(net_file=args.network,
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)
initial_states = env.reset()
ql_agents = {
ts: QLAgent(starting_state=env.encode(initial_states[ts], ts),
state_space=env.observation_spaces(ts),
action_space=env.action_spaces(ts),
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
}
infos = []
done = {'__all__': False}
if args.fixed:
while not done['__all__']:
_, _, done, _ = env.step({})
else:
while not done['__all__']: