Multi-agent reinforcement learning---the learning of behavior when the environment's other agents learn too---moved from Tan's independent learners and Littman's Markov games framework through the cooperative dynamics' analyses and the surveys' question to the deep era's communication, actor-critics, value decompositio...
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
This work proposes AutoVerifier, a residual-guided non-parametric optimization method that learns biases from recurring verifier errors and promotes them to code modules or prompt guidance only after replay validation detects no direct regressions, keeping accepted updates auditable, editable, and reusable.
A DNs cooperative optimization method based on resource aggregation and RL is proposed, which demonstrates the effectiveness and superiority of the proposed method in ensuring the secure operation of DNs.
Delta (Differential Testing for DRL Agents) is proposed, a novel and comprehensive framework that automatically identifies both safety-critical and optimality bugs in DRL agents and investigates the effectiveness of three offline RL algorithms in generating challenger agents.
Jun-Da He, Jie-Ke Shi, Zhou Yang et al.· Proceedings of the ACM on so...· 0 citations
A persistent fleet of cooperative AI scientist agents that operated continuously for nearly six months is described, term this bounded pattern AI Autopoietic Behavior due to the recurring operational improvement mediated by internal feedback and retained through institutional records with high confidence.
M. Patel, W. Wierson, S. C. Ekker· bioRxiv· 0 citations
This work revisits where capacity should be invested in an offline actor--critic method and proposes LAC (Light Actor, deep Critic), a lightweight deterministic actor that matches the strongest diffusion- and flow-matching baselines while achieving up to 4x lower inference latency.
This paper presents a two-wheeled mobile robot trajectory-tracking controller combining a particle swarm optimization (PSO)-tuned fuzzy logic controller (FLC) with a residual reinforcement learning (RL) correction layer.PSO tuning reduces the global distance error by 35% and the integral absolute error by 44% over the...
Le Ngoc Dung, Luu Hong Quan, Doan Cong Anh· International journal of int...· 0 citations
Reinforcement learning (RL) has achieved remarkable success in various domains, but its "black box" nature poses a significant challenge for real-world deployment. Understanding the rationale behind an RL agent's decisions is crucial for trust, debugging, and improving performance. This paper proposes a novel approach...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to neural network design termed Adaptive Neuron Topology Optimization. The core concept revolves around dynamically adjusting the connectivity topology of a neural network based on real-time monitoring of neuron activity. Traditional neural network topology is static, often hinder...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to neural network design termed Adaptive Neuron Topology Optimization. The core concept revolves around dynamically adjusting the connectivity topology of a neural network based on real-time monitoring of neuron activity. Traditional neural network topology is static, often hinder...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to system optimization leveraging Multi-Agent Reinforcement Learning (MARL). The core concept is to decompose complex systems into multiple intelligent agents, each responsible for controlling a specific subsystem. These agents then collaboratively learn optimal control strategies t...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
MIT News · Artificial Intelligence· news.mit.eduOct 27, 2026
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
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