AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work states that there has been an upsurge of interest and development of RL theory and applications in the communities of stochastic control, stochastic analysis and unsupervised learning.
This paper explores the potential-theoretic viewpoint for core reinforcement learning representations and algorithms under a fixed-policy assumption and may offer a path for improved sample efficiency and formal constraints that can be applied to RL.
Reinforcement learning (RL) occupies a distinct position within machine learning, differing fundamentally from both supervised and unsupervised paradigms. Unlike supervised learning, RL agents receive evaluative feedback – judgment on the consequences of actions – rather than explicit correct answers, and they learn co...
Two new nuisances are derived as fixed points of a self-induced Bellman equation, in which they approximate the maximum Bellman operator by its softmax correspondence, and a debiased estimator is proposed through the Neyman orthogonality and established its asymptotic normality under diverging horizons.
Reward design is a critical yet often underexplored component of reinforcement learning for planning problems. In this work we analyze how different reward shaping strategies affect learning performance when using Proximal Policy Optimization (PPO). We evaluate several reward formulations derived from the objective fun...
This work presents a new class of algorithms named Continuous Actor Critic Learning Automaton (CACLA) that can handle continuous states and actions that is straightforward to implement and an experimental comparison is made between this algorithm and other algorithms that can handle continuous action spaces.
The paper concludes that convergence guarantees degrade in a predictable, representation-dependent order as function approximation grows more expressive, and identifies extending stochastic approximation theory to nonlinear function approximation as the central future research prospect.
Education Innovation
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
A Blog post by NVIDIA on Hugging Face
A Blog post by IBM Research on Hugging Face
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