A reinforcement learning environment is a fail-safe digital practice room where an agent can afford to make mistakes and learn from them without real-world consequences.

Anthropic research shows developers using AI assistance scored 17% lower on comprehension tests when learning new coding ...

New benchmark shows top LLMs achieve only 29% pass rate on OpenTelemetry instrumentation, exposing the gap between ...

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

Learn how to simulate Faraday’s Law in electrodynamics using Python (Part 2)! In this video, we continue our step-by-step tutorial on modeling electromagnetic induction, showing how changing magnetic ...

Dive into Faraday’s Law of Electromagnetic Induction with a practical Python implementation in this first part of our Electrodynamics series. Learn how to simulate and visualize changing magnetic ...

Abstract: Spatial Crowdsourcing (SC) has emerged as a significant paradigm for executing complex real-world projects and tasks, which are often decomposed into interdependent subtasks requiring ...

verl is a flexible, efficient and production-ready RL training library for large language models (LLMs). verl is the open-source version of HybridFlow: A Flexible and Efficient RLHF Framework paper.

Recently, there have been significant research interests in training large language models (LLMs) with reinforcement learning (RL) on real-world tasks, such as multi-turn code generation. While online ...

In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a ...

Abstract: The problem of multiagent encirclement with multiobstacle collision avoidance (EMOCA) has been challenging since it is difficult to balance the tradeoff between surrounding a mobile target ...