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,
min_epsilon=0.005,
decay=decay))
for ts in env.agents
}
infos = []
for agent in env.agent_iter():
s, r, done, info = env.last()
if ql_agents[agent].action is not None:
ql_agents[agent].learn(next_state=env.unwrapped.env.encode(
s, agent),
reward=r)
action = ql_agents[agent].act() if not done else None
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 = []
if args.fixed:
while not done['__all__']:
_, _, done, _ = env.step({})
else:
while not done['__all__']:
actions = {ts: ql_agents[ts].act() for ts in ql_agents.keys()}
alpha = 0.1
gamma = 0.99
decay = 1
runs = 1
env = SumoEnvironment(net_file='nets/4x4-Lucas/4x4.net.xml',
route_file='nets/4x4-Lucas/4x4c1c2c1c2.rou.xml',
use_gui=True,
num_seconds=80000,
max_depart_delay=0)
for run in range(1, 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=alpha,
gamma=gamma,
exploration_strategy=EpsilonGreedy(initial_epsilon=0.05, min_epsilon=0.005, decay=decay)) for ts in env.ts_ids}
infos = []
done = {'__all__': False}
while not done['__all__']:
actions = {ts: ql_agents[ts].act() for ts in ql_agents.keys()}
s, r, done, info = env.step(action=actions)
for agent_id in s.keys():
ql_agents[agent_id].learn(next_state=env.encode(s[agent_id], agent_id), reward=r[agent_id])
env.save_csv('outputs/4x4/ql_test', run)
env.close()