def compute_reward(self, rl_actions, **kwargs):
"""See class definition."""
num_rl = self.k.vehicle.num_rl_vehicles
lane_change_acts = np.abs(np.round(rl_actions[1::2])[:num_rl])
return (rewards.desired_velocity(self) + rewards.rl_forward_progress(
self, gain=0.1) - rewards.boolean_action_penalty(
lane_change_acts, gain=1.0))
def compute_reward(self, rl_actions, **kwargs):
"""See class definition."""
if self.test:
return 0
return - rewards.min_delay_unscaled(self) \
- rewards.boolean_action_penalty(rl_actions >= 0.5, gain=1.0)
def compute_reward(self, rl_actions, **kwargs):
"""See class definition."""
r = 0
if rl_actions is not None:
r = -rewards.boolean_action_penalty(rl_actions >= 0.5, gain=2)
if self.env_params.evaluate:
r += -rewards.min_delay_unscaled(self)
#print(f"Reward computed: {r}, rl_actions: {rl_actions}")
else:
r += (-rewards.min_delay_unscaled(self) +
rewards.penalize_standstill(self, gain=0.2))
print(f"Reward computed: {r}, rl_actions: {rl_actions}")
return r
def compute_reward(self, rl_actions, **kwargs):
"""See class definition."""
if self.env_params.evaluate:
return -rewards.min_delay_unscaled(self)
else:
"""
print('delay penalty:', -rewards.min_delay_unscaled(self))
print('standstill penalty:', rewards.penalize_standstill(self, gain=0.2))
print('action penalty:', -rewards.boolean_action_penalty(rl_actions >= 0.5, gain=0.2))
"""
#return (- rewards.min_delay_unscaled(self) +
# rewards.penalize_standstill(self, gain=0.2))
return (
-rewards.min_delay_unscaled(self) -
rewards.boolean_action_penalty(rl_actions >= 0.5, gain=0.01) -
rewards.waiting_penalty(self, gain=0.01))
def test_boolean_action_penalty(self):
"""Test the boolean_action_penalty method."""
actions = [False, False, False, False, False]
self.assertEqual(boolean_action_penalty(actions, gain=1), 0)
self.assertEqual(boolean_action_penalty(actions, gain=2), 0)
actions = [True, False, False, False, False]
self.assertEqual(boolean_action_penalty(actions, gain=1), 1)
self.assertEqual(boolean_action_penalty(actions, gain=2), 2)
actions = [True, False, False, True, False]
self.assertEqual(boolean_action_penalty(actions, gain=1), 2)
self.assertEqual(boolean_action_penalty(actions, gain=2), 4)
def compute_reward(self, rl_actions, **kwargs):
"""See class definition."""
# penalize traffic light changes for occurring
return -rewards.boolean_action_penalty(rl_actions >= 0.5, gain=1.0)
