Dot Physics on MSN

Learn Python for physics lesson 1: Hello world and variables

Master the right-hand rule in physics with this easy-to-follow tutorial! In this video, we explain how to use the right-hand rule correctly to determine directions of magnetic fields, currents, and ...
Dot Physics on MSN

Python physics lesson 19: Learn how Monte Carlo approximates pi

Explore Python Physics Lesson 19 and learn how the Monte Carlo method can approximate Pi with simple yet powerful simulations. In this lesson, we break down the Monte Carlo technique step by step, ...
IEEE

Explainable and Safety Aware Deep Reinforcement Learning-Based Control of Nonlinear Discrete-Time Systems Using Neural Network Gradient Decomposition

Abstract: This paper presents an explainable deep-reinforcement learning (DRL)-based safety-aware optimal adaptive tracking (SOAT) scheme for a class of nonlinear discrete-time (DT) affine systems ...

Quesma Releases OTelBench: Independent Benchmark Reveals Frontier LLMs Struggle with Real-World SRE Tasks

New benchmark shows top LLMs achieve only 29% pass rate on OpenTelemetry instrumentation, exposing the gap between ...
North Penn Now

Machine Learning Using Python: A Complete Learning Path With Practical Projects

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...
Analytics Insight

Top 10 AI Engineer Courses in 2026 for Quick Job Readiness

Overview The best AI engineer courses 2026 focus on building real, job-ready projects.Combining AI engineering basics with LLM engineering leads to stronger car ...
Frontiers

Spike-based Q-learning in a non-von Neumann architecture

Reinforcement learning (RL) provides a computational framework in which an agent learns optimal policies by interacting with the environment and receiving feedback in the form of rewards (Sutton and ...
FinanceFeeds

Crypto Machine Learning Algorithms Explained for Beginners

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...
IEEE

Encoding High-Level Knowledge in Offline Multi-Agent Reinforcement Learning Using Reward Machines

Abstract: Offline reinforcement learning (RL) learns policies from fixed-size datasets without interacting with the environment, while multi-agent reinforcement learning (MARL) faces challenges from ...
GitHub

R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning

This repository contains the official implementation for R3DM accepted at the International Conference on Machine Learning (ICML) 2025. It includes the source code for the ACORM and R3DM algorithms, ...
GitHub

ReinFlow: Fine-tuning Flow Matching Policy with Online Reinforcement Learning

To fully reproduce our experiments, please refer to ReproduceExps.md. To download our training data and reproduce the plots in the paper, please refer to ...
Microsoft

Multimodal Reinforcement Learning with Agentic Verifier for AI Agents

Agentic reasoning models trained with multimodal reinforcement learning (MMRL) have become increasingly capable, yet they are almost universally optimized using sparse, outcome-based rewards computed ...