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Highway merging control using multi-agent reinforcement learning: exploring centralized and decentralized schemes
No full textThis dissertation addresses critical challenges in autonomous vehicle (AV) control, focusing on complex highway merging control during lane reduction using multi-agent reinforcement learning (MARL). Both centralized and decentralized approaches are presented. For the centralized approach, Proximal Policy Optimization based approach is employed to learn optimal merging policies for a fixed number of vehicles in two platoons. To scale up for the large number of AVs in a typical highway scenario, a decentralized approach is investigated, where each AV acts independently based on local observations. Furthermore, as the number of AVs in a traffic flow can vary, a self-attention network is used to handle the varying number of AVs. Several reward functions are explored and compared, including global speed, local speed, fuel consumption, and ride comfort. Novel quantitative metrics are introduced to evaluate the fairness and efficiency of the learned merging strategies. Both proposed MARL approaches consistently outperform benchmark RL method and a rule-based zipper merge strategy across various metrics, including up to 60.14% increase in traffic flow at higher speeds, among many other advantages. Finally, the generalizability of the framework is demonstrated by training the MARL model using low speed scenario and testing the learned policy using high speed scenario
Detecting Behavioral Changes in an Autism Mouse Model after Treatment with Ergothioneine
No full textAutism Spectrum Disorder (ASD) is a neurological disorder that can impair developmental, behavioral, and social skills. Although there are currently no curative remedies authorized for the treatment of autism spectrum disorder, certain dietary supplements may act as a form of therapy for some of the atypical behaviors associated with this diagnosis.
Ergothioneine (ET) is an amino acid derivative known for its antioxidant properties and its ability to enhance neurological function. Here we utilized ET supplementation to examine the potential benefits it may have on autism phenotypes. A BTBR autistic mouse model was used to test the effects of ET treatment followed by behavioral examination. Quantitative analyses of behavioral changes observed between the treatment group along with controls were performed to determine if ET could improve autism-like behaviors. The long-term goal of this study is to establish the possible application of a safe ET supplementation to ameliorate autistic manifestations in ASD children
Nostalgia & Outrage
No full textCatalog of an exhibition held at the Oakland University Art Gallery, January 13 – March 24, 2024. Contains essay by Dick Goody.Excerpt from essay by Dick Goody: “Nostalgia and outrage” is a state of mind. On the one hand, we long to bask in the memories of a gilded childhood. On the other, we see ourselves on a threshold, walking toward an unknowable, often alarming future. But, really, in all of this, how far do we ever stray from the path of our childhood? As Aristotle said: “Give me the child until he is seven and I will show you the foundation of the man.
Empowering Medical Students: Unveiling the Benefits of Nutrition Seminars and Plant-Based Diets in Medical School Education
No full textExtraction of information from ai design decisions - theoretical and practical case study
No full textIn recent years, the emergence of Artificial Intelligence (AI) has revolutionized numerous fields [1], such as the healthcare and automotive industries, and the technologies that use AI have become part of our daily routines. This surge in AI use in different fields has raised many issues because of a lack of transparency regarding the black-box nature of the AI algorithms and, specifically, how the neurons of the neural networks in the AI algorithms interact to conclude outcomes [2]. This problem is especially notable in the automotive industry, where AI vehicles increasingly integrate AI into their systems, especially the Advanced Driver Assistance Systems (ADAS), to improve safety and driver convenience. For engineers, consumers, and regulators, its essential to understand the reasoning behind AI decision-making for many reasons. Accountability, among many other reasons, is considered significant in this context; for example, who will be responsible for accidents when they happen? Is it the person behind the steering wheel or the car maker? [3]. Because of those reasons, the significance of revealing the AI decision-making process by extracting information from AIs decision-making algorithms cannot be overstated, especially in the context of ADAS and, more specifically, in Rear-End Collision Avoidance (RECA) systems. This thesis is broken down into several sections: 1. We trained a reinforcement learning agent to operate a Rear-End Collision Avoidance (RECA) System using the Reinforcement Learning toolbox of MATLAB [4]. We used the code and Simulink model of the example of the Collision Avoidance System (Train DDPG Agent for Adaptive Cruise Control) in the Reinforcement Learning toolbox in MATLAB [5] and the car dynamics of the example of the Adaptive Cruise Control System Using Model Predictive Control in the Model Predictive Control Toolbox in MATLAB [6]. Then, we modified that examples code and Simulink model to fit my application. 2. We developed MATLAB code to extract the trained agent from the Reinforcement Learning (RL) algorithm. 3. We verified the extracted RL agent by simulating it using Simulink and then without the RL agent to confirm its necessity for the RECA system. 4. We formulated the mathematical relationship between the trained agents inputs and output by fitting it to a Linear Regression Model (LRM) using a MATLAB function called fitlm [7]. 5. We verified the LRM by simulating it using Simulink. 6. We used two scaled robotic cars to verify the LRM to confirm the reliability of the extracted information. 7. We analyzed the LRM using the State Space Control technique to determine the basis function and better understand the foundation control law of the RL algorithm. 8. In the last part, we verified the basis functions formula by simulating it using Simulink and then simulating it by excluding some of the basis functions as an extra step to confirm the necessity of all the basis functions in the set. This in-depth theoretical and practical analysis is a significant step towards unraveling the ambiguity of AI algorithms in different fields, especially in the automotive field, by shedding light on the black box of AI algorithms and complex decision-making processes of AI