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Amazon Q’s MCP Flaw Is an Industry Warning: AI Tools Still Lack Workspace Trust Standards

CVE-2026-12957 in Amazon Q is the third MCP auto-execution vulnerability in three AI coding tools. The pattern reveals 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 ...
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 ...
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 ...
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 ...