From f443e3820c74aa4661f8a1f83db5c24efc216452 Mon Sep 17 00:00:00 2001
From: Jaeyoung Lee <jaeyoung.lee@uwaterloo.ca>
Date: Wed, 6 Feb 2019 13:46:44 -0500
Subject: [PATCH] Wait (and the others slightly) improved.
---
options/simple_intersection/maneuvers.py | 43 +++++++++++++-----------
1 file changed, 24 insertions(+), 19 deletions(-)
diff --git a/options/simple_intersection/maneuvers.py b/options/simple_intersection/maneuvers.py
index e995a19..2cf2e7f 100644
--- a/options/simple_intersection/maneuvers.py
+++ b/options/simple_intersection/maneuvers.py
@@ -172,7 +172,7 @@ class Stop(ManeuverBase):
self._penalty_in_violation))
self._LTL_preconditions.append(
- LTLProperty("G ( not stopped_now U in_stop_region )", 100,
+ LTLProperty("G ( not stopped_now U in_stop_region )", 200,
self._enable_low_level_training_properties))
self._LTL_preconditions.append(
@@ -228,7 +228,7 @@ class Stop(ManeuverBase):
elif (rd.speed_limit / 5 < self._v_ref) and \
(self.env.ego.v < self._v_ref / 2) and self.env.ego.acc < 0:
- self._extra_r_terminal = -100
+ self._extra_r_terminal = -200
return True
else:
@@ -241,13 +241,13 @@ class Stop(ManeuverBase):
class Wait(ManeuverBase):
_reward_in_goal = None
- _terminate_in_goal = True
+ _terminate_in_goal = False
def _init_LTL_preconditions(self):
-
self._LTL_preconditions.append(
- LTLProperty("G ( (in_stop_region and stopped_now) U (highest_priority and intersection_is_clear))",
- None, not self._enable_low_level_training_properties)) # not available in low-level training...
+ LTLProperty(
+ "G ( (in_stop_region and has_stopped_in_stop_region) U (highest_priority and intersection_is_clear))",
+ None, not self._enable_low_level_training_properties)) # not available in low-level training...
self._LTL_preconditions.append(
LTLProperty("G ( not (in_intersection and highest_priority and intersection_is_clear) )",
@@ -255,13 +255,13 @@ class Wait(ManeuverBase):
self._LTL_preconditions.append(
LTLProperty(
- "G ( in_stop_region U (highest_priority and intersection_is_clear) )", 150,
+ "G ( in_stop_region U (highest_priority and intersection_is_clear) )", 200,
self._enable_low_level_training_properties))
- self._LTL_preconditions.append(
- LTLProperty(
- "G ( (lane and target_lane) or (not lane and not target_lane) )",
- 150, self._enable_low_level_training_properties))
+ #self._LTL_preconditions.append(
+ # LTLProperty(
+ # "G ( (lane and target_lane) or (not lane and not target_lane) )",
+ # 150, self._enable_low_level_training_properties))
def _init_param(self):
self._update_param()
@@ -270,8 +270,10 @@ class Wait(ManeuverBase):
def _update_param(self):
if self.env.ego.APs['highest_priority'] and self.env.ego.APs['intersection_is_clear']:
self._v_ref = rd.speed_limit
+ self._extra_action_weights_flag = True
else:
self._v_ref = 0
+ self._extra_action_weights_flag = False
def generate_learning_scenario(self):
n_others = 0 if np.random.rand() <= 0 else np.random.randint(1, 4)
@@ -298,17 +300,20 @@ class Wait(ManeuverBase):
self.env.ego.waited_count = np.random.randint(min_waited_count, max_waited_count + 21)
self.env.init_APs(False)
+ self.env._terminate_in_goal = False
self._reward_in_goal = 200
self._enable_low_level_training_properties = True
- self._extra_action_weights_flag = True
+ self._extra_action_weights_flag = False
@property
def extra_termination_condition(self):
if self._enable_low_level_training_properties: # activated only for the low-level training.
- if self.env.ego.APs['highest_priority'] and self.env.ego.APs['intersection_is_clear'] \
- and np.random.rand() <= 0.1 and self.env.ego.v <= self._v_ref / 10 \
- and self.env.ego.acc < 0:
- self._extra_r_terminal = - 100
+ if self.env.ego.APs['highest_priority'] and \
+ self.env.ego.APs['intersection_is_clear'] and \
+ np.random.rand() <= 0.25 and \
+ self.env.ego.v <= self._v_ref / 10 and \
+ self.env.ego.acc < 0:
+ self._extra_r_terminal = -200
return True
else:
self._extra_r_terminal = None
@@ -449,6 +454,7 @@ class ChangeLane(ManeuverBase):
self._violation_penalty_in_low_level_training = 150
self._enable_low_level_training_properties = True
self._extra_action_weights_flag = True
+ self.env._terminate_in_goal = False
def generate_validation_scenario(self):
self.generate_scenario(
@@ -460,6 +466,8 @@ class ChangeLane(ManeuverBase):
self._reward_in_goal = 200
self._violation_penalty_in_low_level_training = 150
self._enable_low_level_training_properties = True
+ self.env._terminate_in_goal = False
+
@staticmethod
def _features_dim_reduction(features_tuple):
@@ -511,9 +519,6 @@ class Follow(ManeuverBase):
self._enable_low_level_training_properties = True
self._extra_action_weights_flag = True
- def generate_validation_scenario(self):
- self.generate_learning_scenario()
-
def _init_param(self):
self._set_v_ref()
--
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