Finally, if a good event doesn't happen (with probability Intuitively this means the most diluted / scattered region. Here we additionally use Mujoco, a commonly used benchmark, to validate the performance. Mujoco is a very smooth task and doesn't contain many local minima, so traditional methods work In Tab. 3, we can see that in easier tasks like Reacher and Pusher, Table 3: Results for Mujoco with replanning frequency of 5. We see that Results for MiniWorld tasks for different methods using a learned PETS transition model. To loosely approximate the Lipschitz constant in our analysis from Sec. 5.3, we simply check all pairwise Lipschitz constants between existing samples (candidate trajectories) in the tree node However, in most cases it still continues to decrease over time. While this is consistent with our qualitative analysis in Sec.

Accurate real-time traffic state forecasting plays a pivotal role in traffic control research. In particular, the CIRCLES consortium project necessitates predictive techniques to mitigate the impact of data source delays. After the success of the MegaVanderTest experiment, this paper aims at overcoming the current system limitations and develop a more suited approach to improve the real-time traffic state estimation for the next iterations of the experiment. In this paper, we introduce the SA-LSTM, a deep forecasting method integrating Self-Attention (SA) on the spatial dimension with Long Short-Term Memory (LSTM) yielding state-of-the-art results in real-time mesoscale traffic forecasting. We extend this approach to multi-step forecasting with the n-step SA-LSTM, which outperforms traditional multi-step forecasting methods in the trade-off between short-term and long-term predictions, all while operating in real-time.