traci.trafficlight.Phase(32, "rrGrrrrrGrrr"),
traci.trafficlight.Phase(2, "rryrrrrryrrr"),
traci.trafficlight.Phase(32, "rrrGGrrrrGGr"),
traci.trafficlight.Phase(2, "rrryyrrrryyr"),
traci.trafficlight.Phase(32, "rrrrrGrrrrrG"),
traci.trafficlight.Phase(2, "rrrrryrrrrry")
])
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=21)
done = False
if args.fixed:
while not done:
_, _, done, _ = env.step({})
else:
while not done:
action = agent.act(agent.get_features(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
env.save_csv(out_csv, run)
ts:
QLAgent(starting_state=env.encode(initial_states[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(actions=actions)
infos.append(info)
for agent_id in ql_agents.keys():
ql_agents[agent_id].learn(new_state=env.encode(s[agent_id]),
reward=r[agent_id])
env.close()
df = pd.DataFrame(infos)
df.to_csv(
'outputs/4x4grid/c2_alpha{}_gamma{}_decay{}_run{}.csv'.format(
alpha, gamma, decay, run),
index=False)
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()}
s, r, done, _ = env.step(actions=actions)
if args.v:
print('s=', env.radix_decode(ql_agents['t'].state), 'a=',
actions['t'], 's\'=', env.radix_encode(s['t']), 'r=',
r['t'])
for agent_id in ql_agents.keys():
ql_agents[agent_id].learn(new_state=env.encode(
s[agent_id]),
reward=r[agent_id])
])
env = VisualizationEnv(
env=env,
episodic=False,
features_names=['Phase 0', 'Phase 1', 'Elapsed time'] + ['Density lane ' + str(i) for i in range(4)] + ['Queue lane ' + str(i) for i in range(4)],
actions_names=['Phase 0', 'Phase 1']
)
for run in range(1, args.runs+1):
initial_states = env.reset()
ql_agents = {ts: QLAgent(starting_state=env.encode(initial_states),
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}
env.set_agent(ql_agents['t'])
env.add_plot('Epsilon', lambda: ql_agents['t'].exploration.epsilon)
done = False
while not done:
actions = {ts: ql_agents[ts].act() for ts in ql_agents.keys()}
s, r, done, _ = env.step(action=actions['t'])
for agent_id in ql_agents.keys():
ql_agents[agent_id].learn(next_state=env.encode(s), reward=r)
env.close()
env.join()