Abstract: In recent years, reinforcement learning (RL) has made great achievements in artificial intelligence. Proximal policy optimization (PPO) is a representative RL algorithm, which limits the ...

Reinforcement learning (RL) for robotics is often associated with large GPU clusters, distributed infrastructure, and x86-based development environments. Training a humanoid robot with high-fidelity ...

Detailed Summary of Lex Fridman Podcast: AI State-of-the-Art 2026 with Nathan Lambert and Sebastian RaschkaThis episode (YouTube: https://www.youtube.com/watch?v ...

The path planning capability of autonomous robots in complex environments is crucial for their widespread application in the real world. However, long-term decision-making and sparse reward signals ...

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...

Reinforcement learning (RL) has witnessed tremendous advances in recent years, enabling agents to master tasks ranging from video games to robotics. However, designing stable, sample-efficient ...

This week in Project52, I took on one of the most exciting challenges yet: a direct face-off between two of the most powerful reinforcement learning (RL) algorithms — Deep Q-Network (DQN) and Proximal ...

Understanding the quantum control landscape (QCL) is important for designing effective quantum control strategies. In this study, we analyze the QCL for a single two-level quantum system (qubit) using ...

Transformer-based models have revolutionized natural language processing. Models like GPT-4, BERT, and T5 dominate NLP applications in 2024, powering language translation, text summarization, and ...