DATAQUEST

Best AI Careers in India 2026: High-Paying Jobs, Skills & Roadmap

Want to start a career in AI? Explore the top AI jobs in India for 2026, including ML Engineer salaries, required skills like ...
DATAQUEST

Best AI Courses in 2026: Beginner to advanced Pro certifications to boost salary and career in India

Best AI courses 2026 in India including Google, AWS, and MIT certifications. Learn AI from beginner to expert level and boost your salary with top programs.
techxplore

New memristor design uses built-in oxygen gradient to bring stability to reinforcement learning

In a recent study published in Nature Communications, researchers created a memristor that uses a built-in oxygen gradient to produce slow, stable conductance changes, enabling a reinforcement ...
marktechpost

A Coding Implementation to Train Safety-Critical Reinforcement Learning Agents Offline Using Conservative Q-Learning with d3rlpy and Fixed Historical Data

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 ...
IEEE

Inverse Q-Learning Optimal Control for Takagi–Sugeno Fuzzy Systems

Abstract: Inverse reinforcement learning optimal control is under the framework of learner–expert, the learner system can learn expert system's trajectory and optimal control policy via a ...
Scientific Research Publishing

Kumar, A., Zhou, A., Tucker, G. and Levine, S. (2020) Conservative Q-Learning for Offline Reinforcement Learning. Advances in Neural Information Processing Systems, 33, 1179-1191.

ABSTRACT: Offline reinforcement learning (RL) focuses on learning policies using static datasets without further exploration. With the introduction of distributional reinforcement learning into ...
marktechpost

Meta AI Introduces MR.Q: A Model-Free Reinforcement Learning Algorithm with Model-Based Representations for Enhanced Generalization

Reinforcement learning (RL) trains agents to make sequential decisions by maximizing cumulative rewards. It has diverse applications, including robotics, gaming, and automation, where agents interact ...
IEEE

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning

Abstract: Offline reinforcement learning (RL), which operates solely on static datasets without further interactions with the environment, provides an appealing alternative to learning a safe and ...