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Human-Aligned Decision Transformers for satellite anomaly response operations for extreme data sparsity scenarios

Human-Aligned Decision Transformers for satellite anomaly response operations for extreme data sparsity scenarios The Moment the Satellite Went Silent It was 3:47 AM on a Tuesday when the telemetry stream from the GEO-7 communications satellite dropped to zero. I was testing a reinforcement learning agent I'd been developing for autonomous satellite operations, and I watched in real-time as my…

On a quiet Tuesday morning, at precisely 3:47 AM, the telemetry stream from satellite GEO-7 abruptly ceased. This author, engrossed in testing a reinforcement learning agent crafted for autonomous satellite operations, observed the trained policy, boasting an impressive 98.7% accuracy in simulated scenarios, freeze. The agent had never encountered a complete telemetry blackout, making this a novel challenge.

The satellite was now out of contact, and the agent was utterly perplexed, prompting the author to delve deeper into the intricacies of satellite anomaly response.

Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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A Finding Is Not a Discovery

This is the first contest I have entered. I built the honesty controls before I built the agent, which is probably backwards for a hackathon and exactly what I wanted to learn from.

Introducing Hy4 Preview

Introducing Hy4 Preview New open weight text input (no vision) LLM from Chinese company Tencent today: 770B total parameters, 49B active parameters, 1M token context window, 1.56TB on Hugging Face .

  • Tencent unveils Hy4, a 770 billion parameter LLM with 49 billion active parameters.
  • Context window of Hy4 extends to one million tokens, a significant increase from Hy3.
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