62 summarised stories about Reinforcement Learning, each linking back to the original source. Browse all topics →
Lilian Weng 8 years ago
A blog post documents policy gradient algorithms, a class of reinforcement learning methods, with updates tracking the addition of new algorithms including SAC, D4PG, TD3, SVPG, IMPALA, and PPG between 2018 and 2021. The post was translated into Korean and Chinese versions. This is a technical reference document on machine learning research rather than reporting on a specific development or discovery.
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TLDRocket
21 July 2026
The neural interface hit a milestone today—BrainCo's brain-computer platform decoded EEG signals into robot control in under 200 milliseconds at Shanghai's AI conference, letting users grip objects through thought alone. But the deeper story emerging across today's developments is about making AI systems work *together* and in *constrained* hardware. An agent-swarm system routing complex code tasks through hierarchical tiers of expensive planner models and cheap worker models reached 80% test pass on implementing SQLite in Rust, outpacing single agents by hours. NVIDIA's new Cosmos 3 Edge—a 4-billion-parameter world model—runs reasoning and robot control locally on edge devices at 15 Hz, eliminating cloud dependency. And Gritt's solar robots now install 3,000 to 4,000 panels daily, generalizing labor tasks across construction work. The pattern is efficiency through specialization: decompose problems, delegate to the right-sized tool, run inference close to the hardware. Google's reportedly etching Gemini directly into silicon for 6 to 10 times greater efficiency, while AMD's Helios rack system—priced higher than Nvidia's but shipping soon—signals compute is fragmenting beyond one vendor's dominance. Meanwhile, mathematicians discovered that AI models like Claude and ChatGPT are now disproving century-old conjectures faster than humans can formalize them, while Chinese models like Kimi K3 rank atop benchmarks, yet Western pricing reflects artificial scarcity rather than cost advantage. As compute becomes available and inference costs fall, frontier labs will pass savings forward. The question is no longer whether AI works—it's whether builders can architect it efficiently, and whether we'll bear the environmental cost of doing so.
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