Python | Machine Learning | Coding | R
66.7K subscribers
1.22K photos
85 videos
151 files
882 links
Help and ads: @hussein_sheikho

Discover powerful insights with Python, Machine Learning, Coding, and R—your essential toolkit for data-driven solutions, smart alg

List of our channels:
https://t.iss.one/addlist/8_rRW2scgfRhOTc0

https://telega.io/?r=nikapsOH
Download Telegram
📕 A Course in Reinforcement Learning by Dimitri P. Bertsekas

Explore the comprehensive world of Reinforcement Learning (RL) with this authoritative textbook by Dimitri P. Bertsekas. This book offers an in-depth overview of RL methodologies, focusing on optimal and suboptimal control, as well as discrete optimization. It's an essential resource for students, researchers, and professionals in the field.

🔗 Download the book here:
https://web.mit.edu/dimitrib/www/RLCOURSECOMPLETE%202ndEDITION.pdf

#ReinforcementLearning #MachineLearning #AI #Bertsekas #FreeEbook #OptimalControl #DynamicProgramming

⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
Please open Telegram to view this post
VIEW IN TELEGRAM
👍144
🤖🧠 NVIDIA, MIT, HKU and Tsinghua University Introduce QeRL: A Powerful Quantum Leap in Reinforcement Learning for LLMs

🗓️ 17 Oct 2025
📚 AI News & Trends

The rise of large language models (LLMs) has redefined artificial intelligence powering everything from conversational AI to autonomous reasoning systems. However, training these models especially through reinforcement learning (RL) is computationally expensive requiring massive GPU resources and long training cycles. To address this, a team of researchers from NVIDIA, Massachusetts Institute of Technology (MIT), The ...

#QuantumLearning #ReinforcementLearning #LLMs #NVIDIA #MIT #TsinghuaUniversity
1
🤖🧠 Agentic Entropy-Balanced Policy Optimization (AEPO): Balancing Exploration and Stability in Reinforcement Learning for Web Agents

🗓️ 17 Oct 2025
📚 AI News & Trends

AEPO (Agentic Entropy-Balanced Policy Optimization) represents a major advancement in the evolution of Agentic Reinforcement Learning (RL). As large language models (LLMs) increasingly act as autonomous web agents – searching, reasoning and interacting with tools – the need for balanced exploration and stability has become crucial. Traditional RL methods often rely heavily on entropy to ...

#AgenticRL #ReinforcementLearning #LLMs #WebAgents #EntropyBalanced #PolicyOptimization
2