This paper provides an in-depth review of deep learning techniques to address the challenges of odometry and global ego-localization using frequency modulated continuous wave (FMCW) radar sensors. In particular, we focus on the prediction of odometry, which involves the determination of the ego-motion of a system by external sensors, and loop closure detection, which concentrates on the determination of the ego-position typically on an existing map. We initially emphasize the significance of these tasks in the context of radar sensors and underscore the motivations behind them. The subsequent sections delve into the practical implementation of deep learning approaches, strategically designed to effectively address the aforementioned challenges. We primarily focus on spinning and automotive radar configurations within the domain of autonomous driving. Additionally, we introduce publicly available datasets that have been instrumental in addressing these challenges and analyze the importance and struggles of current methods used for radar based odometry and localization. In conclusion, this paper highlights the distinctions between the addressed tasks and other radar perception applications, while also discussing their differences from challenges posed by alternative sensor modalities. The findings contribute to the ongoing discourse on advancing radar sensor capabilities through the application of deep learning methodologies, particularly in the context of enhancing odometry and ego-localization for autonomous driving applications.

Rising energy costs and the increasing share of renewable generation create incentives to align production schedules with dynamic electricity prices and on-site solar generation. We address the resulting Multi-Objective Flexible Job Shop Scheduling Problem by proposing a deep reinforcement learning framework that jointly minimizes makespan and energy cost. A single preference-conditioned policy approximates the Pareto front at inference time, eliminating the need to train separate models for different objective weightings. The agent acts as a hyper-heuristic, selecting among heuristic actions at each decision point, including strategies that intentionally delay operations to exploit periods of lower electricity prices or higher solar generation. Preferences are integrated throughout the network via Feature-wise Linear Modulation, while a dual-critic architecture and a diversity loss preserve preference-specific policy behaviors. We evaluate the approach against NSGA-II and Joined Heuristics on synthetic instances ranging from $10\times 5\times 5$ to $15\times 15\times 15$ jobs, operations per job, and machines using normalized hypervolume and inverted generational distance. While NSGA-II performs best on the smallest instances, the proposed approach becomes increasingly competitive as problem size grows and achieves the best results on the largest evaluated instances. These findings indicate promising scalability within the investigated problem range.