I am teaching a robot how to “solve” a maze using DQN. For weeks now it has been converging to possibly the worst policy it possibly could which is to drive backwards into a wall no matter what and accrue enormous negative rewards. I have modulated an enormous amount of variables, hyper-parameters, changed neural network size, drastically altered reward structure in various ways, tried different state inputs, tons of initial exploration, given it memory, made the optimal policy […]

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arXiv:2602.10252v1 Announce Type: new Abstract: Datacenter networks are currently locked in a “tyranny of the single objective”. While modern workloads demand diverse performance goals, ranging from coflow completion times, per-flow fairness, short-flow latencies, existing fabrics are typically hardcoded for a single metric. This rigid coupling ensures peak performance when application and network objectives align, but results in abysmal performance when they diverge. We propose DMart, a decentralized scheduling framework that treats network bandwidth as a competitive marketplace. In […]

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I have been trying to install isaaclab on windows for the past few days and i always end up with the same error everytime. conflicting packages while installing and dll load error while running import h5py i followed pip installation https://isaac-sim.github.io/IsaacLab/main/source/setup/installation/pip_installation.html here i installed recommended nvidia driver, installed vc tools C++ dev, added long path, but still end up with the same error over and over again. Please help while executing pip install “isaacsim[all,extscache]==5.1.0” –extra-index-url https://pypi.nvidia.com ERROR: pip’s […]

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Estimating the state of an environment from high-dimensional, multimodal, and noisy observations is a fundamental challenge in reinforcement learning (RL). Traditional approaches rely on probabilistic models to account for the uncertainty, but often require explicit noise assumptions, in turn limiting generalization. In this work, we contribute a novel method to learn a structured latent representation, in which distances between states directly correlate with the minimum number of actions required to transition between them. The proposed metric space formulation […]

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arXiv:2505.21441v4 Announce Type: replace Abstract: We propose a principled method for autoencoding with random forests. Our strategy builds on foundational results from nonparametric statistics and spectral graph theory to learn a low-dimensional embedding of the model that optimally represents relationships in the data. We provide exact and approximate solutions to the decoding problem via constrained optimization, split relabeling, and nearest neighbors regression. These methods effectively invert the compression pipeline, establishing a map from the embedding space back to […]

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Modern Translation Systems heavily rely on high-quality, large parallel datasets for state-of-the-art performance. However, such resources are largely unavailable for most of the South Asian languages. Among them, Nepali and Tamang fall into such category, with Tamang being among the least digitally resourced languages in the region. This work addresses the gap by developing NepTam20K, a 20K gold standard parallel corpus, and NepTam80K, an 80K synthetic Nepali-Tamang parallel corpus, both sentence-aligned and designed to support machine translation. The […]

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Hierarchical federated learning (HFL) has emerged as a key architecture for large-scale wireless and Internet of Things systems, where devices communicate with nearby edge servers before reaching the cloud. In these environments, uplink bandwidth and latency impose strict communication limits, thereby making aggressive gradient compression essential. One-bit methods such as sign-based stochastic gradient descent (SignSGD) offer an attractive solution in flat federated settings, but existing theory and algorithms do not naturally extend to hierarchical settings. In particular, the […]

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Vertical Federated Learning (VFL) has emerged as a critical paradigm for collaborative model training in privacy-sensitive domains such as finance and healthcare. However, most existing VFL frameworks rely on the idealized assumption of full sample alignment across participants, a premise that rarely holds in real-world scenarios. To bridge this gap, this work introduces Split-MoPE, a novel framework that integrates Split Learning with a specialized Mixture of Predefined Experts (MoPE) architecture. Unlike standard Mixture of Experts (MoE), where routing […]

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