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Traffic signal controller (TSC) has a crucial role in managing traffic flow in urban areas. Recently, reinforcement learning (RL) models have received a great attention for TSC with promising results. However, these RL-TSC models still need to be improved for real-world deployment due to limited exploration of different performance metrics such as fair traffic scheduling or air quality impact. In this work, we introduce a constrained multi-objective RL model that minimizes multiple constrained objectives while achieving a higher expected reward. Furthermore, our proposed RL strategy integrates the peak and average constraint models to the RL problem formulation with maximum entropy off-policy models. We applied this strategy to a single TSC and a network of TSCs. As part of this constrained RL-TSC formulation, we discuss fairness and air quality parameters as constraints for the closed-loop control system optimization model at TSCs calledFAirLight. Our experimental analysis shows that the proposedFAirLightachieves a good traffic flow performance in terms of average waiting time while being fair and environmentally friendly. Our method outperforms the baseline models and allows a more comprehensive view of RL-TSC regarding its applicability to the real world. 

Modern language models have the capacity to store and use immense amounts of knowledge about real-world entities, but it remains unclear how to update such knowledge stored in model parameters. While prior methods for updating knowledge in LMs successfully inject atomic facts, updated LMs fail to make inferences based on injected facts. In this work, we demonstrate that a context distillation-based approach can both impart knowledge about entities and propagate that knowledge to enable broader inferences. Our approach consists of two stages: transfer set generation and distillation on the transfer set. We first generate a transfer set by prompting a language model to generate continuations from the entity definition. Then, we update the model parameters so that the distribution of the LM (the student) matches the distribution of the LM conditioned on the definition (the teacher) on the transfer set. Our experiments demonstrate that this approach is more effective at propagating knowledge updates than fine-tuning and other gradient-based knowledge-editing methods. Moreover, it does not compromise performance in other contexts, even when injecting the definitions of up to 150 entities at once. 

Abstract The near-infrared transmission spectrum of the warm sub-Neptune exoplanet GJ 1214 b has been observed to be flat and featureless, implying a high metallicity atmosphere with abundant aerosols. Recent JWST MIRI Low Resolution Spectrometer observations of a phase curve of GJ 1214 b showed that its transmission spectrum is flat out into the mid-infrared. In this paper, we use the combined near- and mid-infrared transmission spectrum of GJ 1214 b to constrain its atmospheric composition and aerosol properties. We generate a grid of photochemical haze models using an aerosol microphysics code for a number of background atmospheres spanning metallicities from 100 to 1000× solar, as well as a steam atmosphere scenario. The flatness of the combined data set largely rules out atmospheric metallicities ≤300× solar due to their large corresponding molecular feature amplitudes, preferring values ≥1000× solar and column haze production rates ≥10 −10 g cm −2 s −1 . The steam atmosphere scenario with similarly high haze production rates also exhibits sufficiently small molecular features to be consistent with the transmission spectrum. These compositions imply that atmospheric mean molecular weights ≥15 g mol −1 are needed to fit the data. Our results suggest that haze production is highly efficient on GJ 1214 b and could involve non-hydrocarbon, non-nitrogen haze precursors. Further characterization of GJ 1214 b’s atmosphere would likely require multiple transits and eclipses using JWST across the near- and mid-infrared, potentially complemented by ground-based high-resolution transmission spectroscopy.