A new @SciRobotics study highlights a reinforcement learning–based framework that integrates perceptual uncertainty into policy learning to equip humanoid robots with reactive soccer skills and dynamic behaviors.

OPENAI SAYS THAT ON AUGUST 28 IT RESTARTED A LARGE FRONTIER REINFORCEMENT LEARNING RUN THAT WAS PREVIOUSLY PAUSED, AND THAT FOR GPT-5.6 IT IMPROVED THE ROBUSTNESS OF ITS SYSTEM-LEVEL STACK BY ADDING ACTIVATION CLASSIFIERS AND IMPROVING COVERAGE OVER UNIVERSAL JAILBREAKS.

Congratulations to the @GoogleDeepMind authors of "Asynchronous Methods for Deep Reinforcement Learning", recipient of the #ICML2026# Test of Time Award. This work shows that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input.

Congratulations to the authors of "Continuous Control with Deep Reinforcement Learning", recipient of the #ICLR2026# Test of Time Award. This work remains a foundational contribution to robotics. Read the paper: @GoogleDeepMind

I’m often struck by how human self-development mirrors Reinforcement Learning. With strong reward signals from parents, friends, or bosses, we learn fast. With punishment, we avoid mistakes. Sometimes we get stuck in local optima—chasing short-term wins while missing rewards that take years to reveal. What matters most is: 1) the environment we choose, does it gives good signal to us; 2) can we see through the noise to spot true rewards; and are we willing to explore the uncertain—even when the payoff isn’t clear yet? We only live once. Make every exploration count.