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Gathering human feedback
RL-Teacher is an open-source implementation of our interface to train AIs via occasional human feedback rather than hand-crafted reward functions. The underlying technique was developed as a step towards safe AI systems, but also applies to reinforcement learning problems with rewards that are hard to specify.
"أليف" منصة لـ"تبنّي الحيوانات" - دبي بوست
"أليف" منصة لـ"تبنّي الحيوانات" دبي بوست
Tesla Q1 2026 Financial Results and Q&A Webcast - Tesla
Tesla Q1 2026 Financial Results and Q&A Webcast Tesla
Better exploration with parameter noise
We’ve found that adding adaptive noise to the parameters of reinforcement learning algorithms frequently boosts performance. This exploration method is simple to implement and very rarely decreases performance, so it’s worth trying on any problem.
Better exploration with parameter noise
We’ve found that adding adaptive noise to the parameters of reinforcement learning algorithms frequently boosts performance. This exploration method is simple to implement and very rarely decreases performance, so it’s worth trying on any problem.
علاقات المغرب وكازاخستان .. عربة السياسة تسبق حصان الاقتصاد - Hespress
علاقات المغرب وكازاخستان .. عربة السياسة تسبق حصان الاقتصاد Hespress
Proximal Policy Optimization
We’re releasing a new class of reinforcement learning algorithms, Proximal Policy Optimization (PPO), which perform comparably or better than state-of-the-art approaches while being much simpler to implement and tune. PPO has become the default reinforcement learning algorithm at OpenAI because of its ease of use and good performance.
Proximal Policy Optimization
We’re releasing a new class of reinforcement learning algorithms, Proximal Policy Optimization (PPO), which perform comparably or better than state-of-the-art approaches while being much simpler to implement and tune. PPO has become the default reinforcement learning algorithm at OpenAI because of its ease of use and good performance.
تعرف على العلاج بالأكسجين وأهم الأمراض التى يشفيها - اليوم السابع
تعرف على العلاج بالأكسجين وأهم الأمراض التى يشفيها اليوم السابع
Robust adversarial inputs
We’ve created images that reliably fool neural network classifiers when viewed from varied scales and perspectives. This challenges a claim from last week that self-driving cars would be hard to trick maliciously since they capture images from multiple scales, angles, perspectives, and the like.
Robust adversarial inputs
We’ve created images that reliably fool neural network classifiers when viewed from varied scales and perspectives. This challenges a claim from last week that self-driving cars would be hard to trick maliciously since they capture images from multiple scales, angles, perspectives, and the like.
الآلات الذكية.. هل يمكن لأجهزة الحاسوب فهم النصوص؟ - الجزيرة نت
الآلات الذكية.. هل يمكن لأجهزة الحاسوب فهم النصوص؟ الجزيرة نت
Hard Questions: Who Should Decide What Is Hate Speech in an Online Global Community? - meta.com
Hard Questions: Who Should Decide What Is Hate Speech in an Online Global Community? meta.com
فابريس بالوداد رغم انتهاء عقده - أحداث.أنفو
فابريس بالوداد رغم انتهاء عقده أحداث.أنفو
أريد أن أصبح مثقفا - الجزيرة نت
أريد أن أصبح مثقفا الجزيرة نت
Introducing Hard Questions - meta.com
Introducing Hard Questions meta.com
إدخال جهاز إشعاعى جديد لقسم علاج الأورام بطب الإسكندرية بـ4 ملايين جنيه - اليوم السابع
إدخال جهاز إشعاعى جديد لقسم علاج الأورام بطب الإسكندرية بـ4 ملايين جنيه اليوم السابع
Learning from human preferences
One step towards building safe AI systems is to remove the need for humans to write goal functions, since using a simple proxy for a complex goal, or getting the complex goal a bit wrong, can lead to undesirable and even dangerous behavior. In collaboration with DeepMind’s safety team, we’ve developed an algorithm which can infer what humans want by being told which of two proposed behaviors is better.
Learning from human preferences
One step towards building safe AI systems is to remove the need for humans to write goal functions, since using a simple proxy for a complex goal, or getting the complex goal a bit wrong, can lead to undesirable and even dangerous behavior. In collaboration with DeepMind’s safety team, we’ve developed an algorithm which can infer what humans want by being told which of two proposed behaviors is better.
Learning to cooperate, compete, and communicate
Multiagent environments where agents compete for resources are stepping stones on the path to AGI. Multiagent environments have two useful properties: first, there is a natural curriculum—the difficulty of the environment is determined by the skill of your competitors (and if you’re competing against clones of yourself, the environment exactly matches your skill level). Second, a multiagent environment has no stable equilibrium: no matter how smart an agent is, there’s always pressure to get sma...