Abstract: This paper establishes a novel role for Gaussianmixture models (GMMs) as functional approximators of Qfunction losses in reinforcement learning (RL). Unlike the existing RL literature, where ...
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Melissa Etheridge recalls moment 11-year-old son came out to her as straight: 'Mom, I'm sorry' Efforts underway for second round of US-Iran talks as ships reported transiting Strait of Hormuz The only ...
In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We combine ...
ABSTRACT: The current microgrids are experiencing growing difficulties in voltage stability and operational capacity, particularly with constant power loads (CPLs), leading to negative impedance ...
In reinforcement learning (RL), an agent learns to achieve its goal by interacting with its environment and learning from feedback about its successes and failures. This feedback is typically encoded ...
What happens when the strategies that propelled an entire field to unprecedented heights begin to falter? For artificial intelligence, this is no longer a hypothetical question. After years of ...
Accurately estimating the Q-function is a central challenge in offline reinforcement learning. However, existing approaches often rely on a single global Q-function, which struggles to capture the ...
Unmanned surface vehicles (USVs) nowadays have been widely used in ocean observation missions, helping researchers to monitor climate change, collect environmental data, and observe marine ecosystem ...
To provide quantitative analysis of strategic confrontation game such as cross-border trades like tariff disputes and competitive scenarios like auction bidding, we propose an alternating Markov ...
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