Reinforcement Learning Approaches for Traffic Signal Control under Missing Data

The emergence of reinforcement learning (RL) methods in traffic signal control tasks has achieved better performance than conventional rule-based approaches. Most RL approaches require the observation of the environment for the agent to decide which action is optimal for a long-term reward. However, in real-world urban scenarios, missing observation of traffic states may frequently occur due to the lack of sensors, which makes existing RL methods inapplicable on road networks with missing observation. In this work, we aim to control the traffic signals in a real-world setting, where some of the intersections in the road network are not installed with sensors and thus with no direct observations around them. To the best of our knowledge, we are the first to use RL methods to tackle the traffic signal control problem in this real-world setting. Specifically, we propose two solutions: the first one imputes the traffic states to enable adaptive control, and the second one imputes both states and rewards to enable adaptive control and the training of RL agents. Through extensive experiments on both synthetic and real-world road network traffic, we reveal that our method outperforms conventional approaches and performs consistently with different missing rates. We also provide further investigations on how missing data influences the performance of our model.Comment: Published as a conference paper at IJCAI202

LibSignal: An Open Library for Traffic Signal Control

This paper introduces a library for cross-simulator comparison of reinforcement learning models in traffic signal control tasks. This library is developed to implement recent state-of-the-art reinforcement learning models with extensible interfaces and unified cross-simulator evaluation metrics. It supports commonly-used simulators in traffic signal control tasks, including Simulation of Urban MObility(SUMO) and CityFlow, and multiple benchmark datasets for fair comparisons. We conducted experiments to validate our implementation of the models and to calibrate the simulators so that the experiments from one simulator could be referential to the other. Based on the validated models and calibrated environments, this paper compares and reports the performance of current state-of-the-art RL algorithms across different datasets and simulators. This is the first time that these methods have been compared fairly under the same datasets with different simulators.Comment: 11 pages + 6 pages appendix. Accepted by NeurIPS 2022 Workshop: Reinforcement Learning for Real Life. Website: https://darl-libsignal.github.io

(E)-N′-[(2-Hydroxynaphthalen-1-yl)methylidene]nicotinohydrazide

In the mol­ecule of the title compound, C17H13N3O2, the naphthyl ring system and the pyridine ring form a dihedral angle of 12.2 (3)°. An intra­molecular O—H⋯N hydrogen bond generates a six-membered ring with an S(6) ring motif. This also contributes to the relative overall near planarity of the mol­ecule [r.m.s. deviation of all 22 non-H atoms = 0.107 (5) Å]. In the crystal, mol­ecules are linked through inter­molecular N—H⋯N hydrogen bonds, forming chains along the a axis

How Do Price and Quantity Promotions Affect Hedonic Purchases? An ERPs Study

Due to consuming hedonic products unnecessary to basic well-being, consumers need justifications for pleasure. However, different justifications have differential influences in promoting hedonic purchases, such as price and quantity promotions (PP and QP), the difference being that the latter requires purchasing additional units to get the same discount as the former. In the present study, even-related potentials (ERPs) was applied to reveal the timing of brain activities to further understand how promotion information consisting of promotion type (PP and QP) and discount depth, deep and shallow discounts (DD and SD) on hedonic products was processed. Behaviorally, consumers were more willing to purchase items in PP and DD conditions than QP and SD conditions, respectively, and spent more time making final purchase decisions in QP and DD condition or PP and SD condition compared to PP and DD condition. Neurophysiologically, DD automatically recruited more attentional resources than SD and led to a higher P2 amplitude. QP and DD condition or PP and SD condition evoked a larger N2 amplitude and enhanced perceptual conflict compared to PP and DD condition. During late stage, PP and DD elicited a more positive LPP amplitude in contrast to QP and SD, respectively, indicating that people have stronger purchase intention and positive affect in PP and DD contexts. These findings provided evidence for the differential influences between PP and QP and what ultimately made consumers buy hedonic products or not

Insecticidal effect of volatile compounds from plant materials of Murraya exotica against Red Imported Fire Ant Workers

The effect of volatile compounds from the mashed fresh, fallen, and dried leaves of Murraya exotica on the behavior of red imported fire ant (Solenopsis invicta, RIFA) workers was investigated by fumigation toxicity bioassay. The volatile compounds from different mashed leaves (fresh, fallen, and dried leaves) of M. exotica were collected by solid-phase microextraction and identified by gas chromatography–mass spectrometry. β-Caryophyllene, α-cedrene, α-copaene, β-cubebene, and germacrene D were identified as major components of the volatile compounds. In exposure time from 1 d to 9 d, the mortality of RIFA increased from 5.00% to 100.00% (fresh leaves), 11.67% to 93.33% (fallen leaves), and 15.00% to 83.33% (dried leaves) in minor workers, whereas in major workers, the increases were from 13.33% to 93.33% (fresh leaves), 6.67% to 83.33% (fallen leaves), and 10.00% to 60.00% (dried leaves). The volatile compounds reduced the walking and grasping abilities and aggregation rate of RIFA workers. Results indicate that mashed leaves of M. exotica have potential for controlling RIFA

Measurement of the Cotton-Mouton effect in nitrogen, oxygen, carbon dioxide, argon, and krypton with the Q & A apparatus

Experiments for vacuum birefringence and vacuum dichroism have been set up with high-finesse high magnetic experimental apparatuses, which seem to be ideal for small gaseous Cotton-Mouton effect (CME) measurements. PVLAS Collaboration has measured CMEs in krypton, xenon and neon at the wavelength of 1064 nm. In this Letter, we report on our measurement of CMEs in nitrogen, oxygen, carbon dioxide, argon, and krypton at the same wavelength in a magnetic field B = 2.3 T at pressure P = 0.5-300 Torr and temperature T = 295-298 K. Our results agree with the PVLAS results in the common cases.Comment: 8 pages, 2 tables, 5 figures, submitted to Chemical Physics Letters. Some modifications are made in the revision according to the referee's comments: Donotations in equations are unified. Error in quoting numbers in 2 places in Table 2 is corrected. Uncertainty in modulation depth is included in the total systematic error. References are order re-arrange