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."""
# compute the system-level performance of vehicles from a velocity
# perspective
reward = rewards.rl_forward_progress(self,
gain=self.forward_progress_gain)
# Calculate collision reward
collision_r = self.compute_collision_reward(rl_actions)
reward += collision_r
# punish excessive lane changes by reducing the reward by a set value
# every time an rl car changes lanes (10% of max reward)
for veh_id in self.k.vehicle.get_rl_ids():
if self.k.vehicle.get_last_lc(veh_id) == self.time_counter:
reward -= 0.1
return reward
def compute_reward(self, rl_actions, **kwargs):
"""See class definition."""
if self.env_params.evaluate:
return np.mean(self.k.vehicle.get_speed(self.k.vehicle.get_ids()))
else:
# return a reward of 0 if a collision occurred
if kwargs["fail"]:
return 0
# reward high system-level velocities
cost1 = rewards.desired_velocity(self, fail=kwargs["fail"])
# encourage rl to move
cost2 = rewards.rl_forward_progress(self,
gain=1) / (30 * self.num_rl)
# weights for cost1, cost2, and cost3, respectively
eta1, eta2 = 0.50, 0.50
return max(eta1 * cost1 + eta2 * cost2, 0)
