def _apply_rl_actions(self, rl_actions):
"""See class definition."""
if self.env_params.additional_params["use_follower_stopper"]:
for veh_id in rl_actions.keys():
self._av_controller.veh_id = veh_id
self._av_controller.v_des = rl_actions[veh_id][0]
acceleration = self._av_controller.get_action(self)
# Apply the action via the simulator.
self.k.vehicle.apply_acceleration(veh_id, acceleration)
else:
rl_ids = list(rl_actions.keys())
acceleration = get_rl_accel(
accel=[deepcopy(rl_actions[veh_id][0]) for veh_id in rl_ids],
vel=self.k.vehicle.get_speed(rl_ids),
max_accel=self.env_params.additional_params["max_accel"],
dt=self.sim_step,
)
# Run the action through the controller, to include failsafe
# actions.
for i, veh_id in enumerate(rl_ids):
acceleration[i] = self.k.vehicle.get_acc_controller(
veh_id).get_action(self, acceleration=acceleration[i])
# Apply the action via the simulator.
self.k.vehicle.apply_acceleration(acc=acceleration,
veh_ids=list(rl_actions.keys()))
def _apply_per_lane_actions(self, rl_actions, veh_ids):
"""Apply accelerations to RL vehicles on a given lane.
Parameters
----------
rl_actions : array_like
the actions to be performed on the given lane
veh_ids : list of str
the names of the RL vehicles on the given lane
"""
if self.env_params.additional_params["use_follower_stopper"]:
accelerations = []
for i, veh_id in enumerate(veh_ids):
self._av_controller.veh_id = veh_id
self._av_controller.v_des = rl_actions[i]
accelerations.append(self._av_controller.get_action(self))
else:
accelerations = get_rl_accel(
accel=deepcopy(rl_actions),
vel=self.k.vehicle.get_speed(veh_ids),
max_accel=self.env_params.additional_params["max_accel"],
dt=self.sim_step,
)
# Run the action through the controller, to include failsafe
# actions.
for i, veh_id in enumerate(veh_ids):
accelerations[i] = self.k.vehicle.get_acc_controller(
veh_id).get_action(self, acceleration=accelerations[i])
# Apply the actions via the simulator.
self.k.vehicle.apply_acceleration(veh_ids,
accelerations[:len(veh_ids)])
def _apply_rl_actions(self, rl_actions):
"""See class definition."""
if self.env_params.additional_params["use_follower_stopper"]:
accelerations = []
for i, veh_id in enumerate(self.rl_ids()):
self._av_controller.veh_id = veh_id
self._av_controller.v_des = rl_actions[i]
accelerations.append(self._av_controller.get_action(self))
else:
accelerations = get_rl_accel(
accel=deepcopy(rl_actions),
vel=self.k.vehicle.get_speed(self.rl_ids()),
max_accel=self.env_params.additional_params["max_accel"],
dt=self.sim_step,
)
# Run the action through the controller, to include failsafe
# actions.
for i, veh_id in enumerate(self.rl_ids()):
accelerations[i] = self.k.vehicle.get_acc_controller(
veh_id).get_action(self, acceleration=accelerations[i])
# Apply the actions via the simulator.
self.k.vehicle.apply_acceleration(self.rl_ids(),
accelerations[:len(self.rl_ids())])
def _apply_rl_actions(self, rl_actions):
"""See class definition."""
for key in rl_actions.keys():
# Get the lane ID.
lane = int(key.split("_")[-1])
# Get the acceleration for the given lane.
acceleration = get_rl_accel(
accel=deepcopy(rl_actions[key]),
vel=self.k.vehicle.get_speed(self.rl_ids()[lane]),
max_accel=self.env_params.additional_params["max_accel"],
dt=self.sim_step,
)
# Apply the actions to the given lane.
self._apply_per_lane_actions(acceleration, self.rl_ids()[lane])
def step(self, action):
"""Advance the simulation by one step."""
collision = False
done = False
for _ in range(self.sims_per_step):
self.t += 1
# Compute the accelerations.
self.accelerations = self._get_accel(self.speeds, self.headways)
if self.rl_ids is not None and action is not None:
# Compute the accelerations for RL vehicles.
self.accelerations[self.rl_ids] = get_rl_accel(
accel=action,
vel=self.speeds[self.rl_ids],
max_accel=self.max_accel,
dt=self.dt,
)
# Clip by safe, non-negative bounds.
accel_min = -self.speeds[self.rl_ids] / self.dt
accel_max = self._failsafe(self.rl_ids)
self.accelerations[self.rl_ids] = np.clip(
self.accelerations[self.rl_ids],
a_max=accel_max,
a_min=accel_min)
# Update the speeds, positions, and headways.
self.positions, self.speeds = self._update_state(
pos=self.positions,
vel=self.speeds,
accel=self.accelerations,
)
self.headways = self._compute_headway()
if self.gen_emission:
data = {"t": self.t}
data.update({
"pos_{}".format(i): self.positions[i]
for i in range(self.num_vehicles)
})
data.update({
"vel_{}".format(i): self.speeds[i]
for i in range(self.num_vehicles)
})
self._emission_data.append(data)
# Determine whether the rollout is done.
collision = any(self.headways < 0)
done = (self.t >= (self.warmup_steps + self.horizon) *
self.sims_per_step) or collision
if done:
break
if collision:
print("Collision")
info = {}
if self.t > self.warmup_steps * self.sims_per_step:
speed = np.mean(self.speeds)
self._mean_speeds.append(speed)
self._mean_accels.append(np.mean(np.abs(self.accelerations)))
info.update({"v_eq": self._v_eq})
info.update({"v_eq_frac": np.mean(self._mean_speeds) / self._v_eq})
info.update({"v_eq_frac_final": speed / self._v_eq})
info.update({"speed": np.mean(self._mean_speeds)})
info.update({"abs_accel": np.mean(self._mean_accels)})
obs = self.get_state()
if isinstance(obs, dict):
obs = {key: np.asarray(obs[key]) for key in obs.keys()}
else:
obs = np.asarray(self.get_state())
reward = self.compute_reward(action if action is not None else [0])
return obs, reward, done, info