Sample efficiency in Reinforcement Learning (RL) has traditionally been driven by algorithmic enhancements. In this work, we demonstrate that scaling can also lead to substantial improvements. We conduct a thorough investigation into the interplay of scaling model capacity and domain-specific RL enhancements. These empirical findings inform the design choices underlying our proposed BRO (Bigger, Regularized, Optimistic) algorithm. The key innovation behind BRO is that strong regularization allows for effective scaling of the critic networks, which, paired with optimistic exploration, leads to superior performance. BRO achieves state-of-the-art results, significantly outperforming the leading model-based and model-free algorithms across 40 complex tasks from the DeepMind Control, MetaWorld, and MyoSuite benchmarks. BRO is the first model-free algorithm to achieve near-optimal policies in the notoriously challenging Dog and Humanoid tasks.

Sample efficiency in Reinforcement Learning (RL) has traditionally been driven by algorithmic enhancements. In this work, we demonstrate that scaling can also lead to substantial improvements. We conduct a thorough investigation into the interplay of scaling model capacity and domain-specific RL enhancements. These empirical findings inform the design choices underlying our proposed BRO (Bigger, Regularized, Optimistic) algorithm. The key innovation behind BRO is that strong regularization allows for effective scaling of the critic networks, which, paired with optimistic exploration, leads to superior performance.

Sample efficiency in Reinforcement Learning (RL) has traditionally been driven by algorithmic enhancements. In this work, we demonstrate that scaling can also lead to substantial improvements. We conduct a thorough investigation into the interplay of scaling model capacity and domain-specific RL enhancements. These empirical findings inform the design choices underlying our proposed BRO (Bigger, Regularized, Optimistic) algorithm. The key innovation behind BRO is that strong regularization allows for effective scaling of the critic networks, which, paired with optimistic exploration, leads to superior performance. BRO achieves state-of-the-art results, significantly outperforming the leading model-based and model-free algorithms across 40 complex tasks from the DeepMind Control, MetaWorld, and MyoSuite benchmarks. BRO is the first model-free algorithm to achieve near-optimal policies in the notoriously challenging Dog and Humanoid tasks.

Jack Black is reportedly set to play Minecraft Steve. Deadline wrote on Tuesday that the School of Rock actor will play the game's blocky protagonist alongside Jason Momoa in the game's long-delayed film adaptation. The Minecraft adaptation has been in development since 2014. It's cycled through at least three previous directors (Shawn Levy, Rob McElhenney and Peter Sollett) and two missed release windows (2019 and 2022). Its current target date is April 4, 2025.

Most people surveyed about autonomous are comfortable with the technology and yet billions continue ... [ ] to be invested in the technology. In a recent survey by Myplanet of various technologies, "autonomous driving" came in as the most uncomfortable of the thirty-five technologies at 66.8% of the Americans surveyed. To put that in perspective, one of the technologies near the middle of the pack was "surgical robot" at 42% negative, which translates into "I'd rather your'bot cuts me open than have it drive me to the corner store." As summarized well by Jason Cottrell, Myplanet CEO, "Customers have made up their minds about autonomous driving and it's skewed heavily to the negative." Other studies, in fact, corroborate that level of fear (e.g.

If you have ever played a video game, no matter what era you played it in, you have interacted with artificial intelligence. Regardless of whether you prefer race-car games like Gran Turismo, strategy games like God Of War, or shooting games like Call Of Duty, you will always find elements controlled by AI. Even things that you don't think would be AI controlled, are! AIs are often behind the characters you typically don't pay much attention to, such as enemy creeps, neutral merchants, or even animals and other background characters. When it comes to video games, artificial intelligence has grown leaps and bounds, allowing us to have some of the most realistic gameplay experiences yet.

It's been awhile since we've talked about the remote controlled robot jockeys used in Arabian camel racing, but a recent scandal that has rocked the camel-racing world compels us to revisit the topic. The Dubai police discovered that some shady characters have been selling robot jockeys equipped with stun guns to "encourage" camels to run faster. We're pretty sure that the animals don't need any more incentive to run -- they already have a robot whipping them -- and it's good to see that the powers-that-be agree with us, as the two men selling the machines were arrested. Now that our dromedary friends need no longer fear being tased in the name of sport, we only have to worry about over-zealous peace officers using them on all of us.

Whether we realize it or not, machine learning touches our daily lives in many ways. When you upload a picture on social media, for example, you might be prompted to tag other people in the photo. That's called image recognition, a machine learning capability by which the computer learns to identify facial features. Other examples include number and voice recognition applications. From an intrusion detection perspective, analysts can apply machine learning, data mining and pattern recognition algorithms to distinguish between normal and malicious traffic.