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1,990 papers
Project TALOS is an autonomous research intelligence platform powered by deep reinforcement learning (DDDQN), multi-tier LLM orchestration, and the Grey Wolf Optimizer (GWO). It conducts end-to-end scientific literature discovery and evaluation across 18 academic APIs.
An error management climate (EMC) promotes open discussion, analysis, and constructive handling of errors, fostering learning and innovation. However, how to develop such a climate in construction organizations remains poorly understood. This paper examines practical mechanisms for cultivating EMC in project environm...
Project TALOS is an autonomous research intelligence platform powered by deep reinforcement learning (DDDQN), multi-tier LLM orchestration, and the Grey Wolf Optimizer (GWO). It conducts end-to-end scientific literature discovery and evaluation across 18 academic APIs.
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An action is an archetype: substantiated structure for how a thing is done, laid down by what doing it has resolved or produced. This paper states that commitment formally within the Language of Stress, a value-primitive theory of consciousness, and follows what it does. It is the treatment that the constraint map for...
This paper is a narrative literature review examining Dynamic Difficulty Adjustment (DDA) in video games. It covers the psychological foundations of DDA (Flow Theory and Self-Determination Theory), the main technical approaches used to implement it including rule-based methods, player modeling, reinforcement learning,...
Project TALOS is an autonomous research intelligence platform powered by deep reinforcement learning (DDDQN), multi-tier LLM orchestration, and the Grey Wolf Optimizer (GWO). It conducts end-to-end scientific literature discovery and evaluation across 18 academic APIs.
Project TALOS is an autonomous research intelligence platform powered by deep reinforcement learning (DDDQN), multi-tier LLM orchestration, and the Grey Wolf Optimizer (GWO). It conducts end-to-end scientific literature discovery and evaluation across 18 academic APIs.
{ "upload_type": "software", "title": "CTAM-RL: Continual Threat-Aware Memory for Continual Reinforcement Learning in Autonomous Cyber Defense (v1.1.0)", "description": "Code, extracted attack profiles, and per-seed results for the manuscript 'A Continual Reinforcement Learning Framework for Autonomous Cyber Defense Un...
Safety-Aware DQN Variants for Lightweight Sensor-Based Collision Avoidance in Autonomous Vehicles This repository contains the official research software, experiment code, and datasets supporting the manuscript "Safety-Aware DQN Variants for Lightweight Sensor-Based Collision Avoidance in Autonomous Vehicles." Paper In...
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
The effort aims to help U.S. learners from kindergarten to community college, with an emphasis on math, making, and the constructive use of AI.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
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