aido1_LF1
: Lane Following
aido1_LFV1
: Lane Following with Obstacles
aido1_LF1
aido1_LF1
aido1_LF1
aido1_LF1
Modified 2018-10-30 by julian121266
Modified 2018-10-31 by julian121266
As a performance indicator for both the “lane following task” and the “lane following task with other dynamic vehicles”, we choose the integrated speed $v(t)$ along the road (not perpendicular to it) over time of the Duckiebot. This measures the moved distance along the road per episode, where we fix the time length of an episode. This encourages both faster driving as well as algorithms with lower latency. An episode is used to mean running the code from a particular initial configuration.
$$ \objective_{P-LF(V)}(t) = \int_{0}^{t} - v(t) dt $$
The integral of speed is defined over the traveled distance of an episode up to time $t=T_{eps}$, where $T_{eps}$ is the length of an episode.
The way we measure this is in units of “tiles traveled”:
$$ \objective_{P-LF(V)}(t) = \text{# of tiles traveled} $$
Modified 2018-10-30 by julian121266
In an autonomous mobility-on-demand system a coordinated fleet of robotic taxis serves customers in an on-demand fashion. An operational policy for the system must optimize in three conflicting dimensions:
We consider robotic taxis that can carry one customer. To compare different AMoD system operational policies, we introduce the following variables:
\begin{align*} &d_E &= &\text{ empty distance driven by the fleet} \\ &d_C &= &\text{ occupied distance driven by the fleet} \\ &d_T = d_C + d_E &= &\text{ total distance driven by the fleet} \\ &N &= &\text{ fleet size} \\ &R &= &\text{ number of customer requests served} \\ &w_i &= &\text{ waiting time of request } i\in \{1,...,R\} \\ &W &= &\text{ total waiting time } W = \sum_{i=1}^{R} w_i \end{align*}
The provided simulation environment is designed in the standard reinforcement framework: Rewards are issued after each simulation step. The (undiscounted) sum of all rewards is the final score. The higher the score, the better the performance.
For the AMoD-Task, there are 3 different championships (sub-tasks) which constitute separate competitions. The simulation environment computes the reward value for each category and conatenates them into a vector of length 3, which is then communicated as feedback to the learning agent. The agent can ignore but the entry of the reward vector from the category that they wish to maximize.
The three championships are as follows:
Modified 2018-10-21 by clruch
In the Service Quality Championship, the principal goal of the operator is to provide the highest possible service quality at bounded operational cost. Two negative scalar weights $\alpha_1\lt{}0$ and $\alpha_2\lt{}0$ are introduced. The performance metric to maximize is
\begin{align*} \mathcal{J}_{P-AMoD,1} = \alpha_1 W + \alpha_2 d_E \end{align*}
The values $\alpha_1$ and $\alpha_2$ are chosen such that the term $W$ dominantes the metric. The number of robotic taxis is fixed at some fleet size $\bar{N} \in \mathbb{N}_{\gt{}0}$.
Modified 2018-10-21 by clruch
In the Efficiency Championship, the principal goal of the operator is to perform as efficiently as possible while maintaining the best possible service level. Two negative scalar weights $\alpha_3\lt{}0$ and $\alpha_4\lt{}0$ are introduced. The performance metric to maximize is
\begin{align*} \mathcal{J}_{P-AMoD,2} = \alpha_3 W + \alpha_4 d_E \end{align*}
$\alpha_3$ and $\alpha_4$ are chosen such that the term $d_E$ dominantes the metric. The number of robotic taxis is fixed at some fleet size $\bar{N} \in \mathbb{N}_{\gt{}0}$.
Modified 2019-01-17 by Jacopo Tani
In the Fleet Size Championship, the goal is to reduce the fleet size as much as possible while keeping the total waiting time $W$ below a fixed level $\bar{W}\gt{}0$. The condition $W\leq\bar{W}$ is equivalent to guaranteeing average waiting times smaller than $\frac{\bar{W}}{R}$. Therefore, the performance score to maximize is
\begin{align*} \mathcal{J}_{P-AMoD,3} = \begin{cases} -N & \text{if }W\leq\bar{W} \\ -\infty & \text{else} \end{cases} \end{align*}
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