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Time Slotted Channel Hopping (TSCH) is a widely adopted Media Access Control (MAC) protocol within the IEEE 802.15.4e standard, designed to provide reliable and energy-efficient communication in Industrial Internet of Things (IIoT) networks. However, state-of-the-art TSCH schedulers rely on static slot allocations, resulting in idle listening and unnecessary power consumption under dynamic traffic conditions. This paper introduces RL-ASL, a reinforcement learning-driven adaptive listening framework that dynamically decides whether to activate or skip a scheduled listening slot based on […]

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RL involves exploration, search, planning, etc. Which of these steps could eventually be made much more performant with quantum computers, assuming the economics of said computers became realistic en masse? Off the cuff, maybe something like MCTS? submitted by /u/thecity2 [link] [comments]

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Hello everyone, I wanted to share a project I am working on for a hackathon. It’s a reinforcement learning environment where an AI agent acts as a power grid operator. I’ve tried to keep physics and maths as real as possible. Github repo link : https://github.com/krishnagoyal099/Opengrid_env Live link : https://huggingface.co/spaces/K446/Opengrid I would really like to get your feedback on the physics modeling and reward structure, and also if anyone manages to solve the “hard” task! I am willing […]

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Harnessing the predictive capability of Markov process models requires propagating probability density functions (beliefs) through the model. For many existing models however, belief propagation is analytically infeasible, requiring approximation or sampling to generate predictions. This paper proposes a functional modeling framework leveraging sparse Sum-of-Squares (SoS) forms for valid (conditional) density estimation. We study the theoretical restrictions of modeling conditional densities using the SoS form, and propose a novel functional form for addressing such limitations. The proposed architecture enables […]

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These lecture notes provide a comprehensive framework for performing global statistical fits in high-energy physics using modern Machine Learning (ML) surrogates. We begin by reviewing the statistical foundations of model building, including the likelihood function, Wilks’ theorem, and profile likelihoods. Recognizing that the computational cost of evaluating model predictions often renders traditional minimization prohibitive, we introduce Boosted Decision Trees to approximate the log-likelihood function. The notes detail a robust ML workflow including efficient generation of training data with […]

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Hi, I am looking to do some project in robotics stimulation in the area of reinforcement learning. Can someone give me any good ideas as well as resources/platform to do so. I found one named Mojuco, but cannot find any good videos on that. submitted by /u/Southern_Reserve2609 [link] [comments]

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Hi everyone. This is my first question in Reddit, so I do not know if this the place to publish it. I have been trying to train a PPO model to make a Hopper agent “walk”. I have implemented my own version of the PPO algorithm, so that I can modify the architecture more easily. I have done already a huge hyperparameter search (manually done), changed the reward function to an easier and also more complex one, chatted […]

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Message Passing Neural Networks have recently become the most popular approach to graph machine learning tasks; however, their receptive field is limited by the number of message passing layers. To increase the receptive field, Graph Transformers with global attention have been proposed; however, global attention does not take into account the graph topology and thus lacks graph-structure-based inductive biases, which are typically very important for graph machine learning tasks. In this work, we propose an alternative approach: cluster […]

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