def execute_action(self):
selected_actions = self.model_action.get_selected_results_with_index()
if selected_actions and self.args_for_action:
for name, _, act_idx in selected_actions:
try:
action = self.actions[act_idx]
if action:
action.act([arg for arg, _, _ in self.args_for_action], self)
except Exception as e:
debug.log("execute_action", e)
def execute_action(self):
selected_actions = self.model_action.get_selected_results_with_index()
if selected_actions and self.args_for_action:
for name, _, act_idx in selected_actions:
try:
action = self.actions[act_idx]
if action:
action.act([arg for arg, _, _ in self.args_for_action],
self)
except Exception as e:
debug.log("execute_action", e)
def step(self, action_dict):
assert self.env_state == EnvState.NORMAL, "env.env_state is not EnvState.NORMAL"
try:
self.env_state = act(self, self.EGO_VEH_ID, action_dict)
if self.env_state == EnvState.DONE:
obs_dict = self.obs_dict_hist.get(-1)
veh_dict = self.veh_dict_hist.get(-1)
else:
obs_dict = get_obs_dict(self)
veh_dict = get_veh_dict(self)
if self.agt_ctrl == False:
action_dict = infer_action(self)
self.action_dict_hist.add(action_dict)
self.veh_dict_hist.add(veh_dict)
self.obs_dict_hist.add(obs_dict)
except (traci.FatalTraCIError, traci.TraCIException):
self.env_state = EnvState.ERROR
raise
info = action_dict
return obs_dict, get_reward_list(self), self.env_state, info
def step(self, action_dict=None):
assert self.env_state == EnvState.NORMAL, "env.env_state is not EnvState.NORMAL"
try:
self.env_state = act(self, self.EGO_VEH_ID, action_dict)
# if ego reaches the end of an incorrect (turning) lane, simulation is considered as DONE
if self.env_state == EnvState.NORMAL and \
self.obs_dict_hist[-1]["ego_dist_to_end_of_lane"] < 0.01 and \
self.obs_dict_hist[-1]["ego_correct_lane_gap"] != 0:
self.env_state = EnvState.DONE
if self.env_state == EnvState.DONE:
obs_dict = deepcopy(self.obs_dict_hist[-1])
veh_dict = deepcopy(self.veh_dict_hist[-1])
else:
obs_dict = get_obs_dict(self)
veh_dict = get_veh_dict(self)
self.veh_dict_hist.append(veh_dict)
self.obs_dict_hist.append(obs_dict)
if self.agt_ctrl == False:
action_dict = infer_action(self)
self.action_dict_hist.append(action_dict)
info = action_dict
"""
print(self.obs_dict_hist[-1]["veh_ids"])
print("peer", self.obs_dict_hist[-1]["veh_relation_peer"])
print("conflict", self.obs_dict_hist[-1]["veh_relation_conflict"])
print("ahead", self.obs_dict_hist[-1]["veh_relation_ahead"])
print("next", self.obs_dict_hist[-1]["veh_relation_next"])
print("in_intersection", self.obs_dict_hist[-1]["in_intersection"])
print("has_priority", self.obs_dict_hist[-1]["has_priority"])
print("ego_has_priority", self.obs_dict_hist[-1]["ego_has_priority"])
print(self.obs_dict_hist[-1]["ttc"])
print(get_reward_list(self)[0][1])
"""
return obs_dict, get_reward_list(self), self.env_state, info
except (traci.FatalTraCIError, traci.TraCIException):
self.env_state = EnvState.ERROR
raise
def like(request):
return act(request, db_adapter.like)
def dislike(request):
return act(request, db_adapter.dislike)