Social Media Ideas for Reinforcement-Learning

Reinforcement Learning from Scratch (Part 3): Policies, Value Functions, Bellman Equations, and…

In the previous article, we learned how Markov Decision Processes (MDPs) model sequential decision-making problems. Now, the next question…Continue reading on Medium »

How Reinforcement Learning Is Transforming Algorithmic Trading

Delayed reward information is underweighted in reinforcement learning with dispersed feedback

by Miruna Cotet, David Poensgen, Ian Krajbich Learning is fundamental to adaptive behavior. In the typical learning task, each action is associated with only one outcome, which co...

Temporal Difference Learning and Policy Gradient Optimization Fields: Engineering Native MQL5 Reinforcement Learning Arc

The Fundamental Choice in Reinforcement Learning: On‑Policy vs. Off‑Policy

How a simple choice shapes exploration, safety, and efficiency The post The Fundamental Choice in Reinforcement Learning: On‑Policy vs. Off‑Policy appeared first on Towards Data Sc...

A very simple explanation of The Gambler’s Problem in Reinforcement Learning

This article is based on Example 4.3 from Sutton and Barto’s Reinforcement Learning: An Introduction, one of the most widely read…Continue reading on Medium »

A Practical Guide to Implementing the REINFORCE Algorithm in Python (Part 5)

Learn how to build the REINFORCE algorithm from scratch using Python, PyTorch, and Gymnasium with a step-by-step, beginner-friendly…Continue reading on Medium »

What Is a Markov Decision Process? How Machines Make Smart Decisions

AI Update & MoreContinue reading on Medium »

Xiaohongshu's Evolving-RL: A New Paradigm for Self-Evolving AI Agent Skills

Researchers from Xiaohongshu (RED), the influential Chinese lifestyle and social commerce platform, have published Evolving-RL, a novel reinforcement learning framework that enable...

Best practices for multi-turn reinforcement learning in Amazon SageMaker AI

In this post, we share best practices for reliable multi-turn RL training. We cover how to build a training environment you can trust, set up an external evaluation, design a rewar...

Flexible goal learning involves coordinated population activity in dCA1 and medial orbitofrontal cortex

by Jiasong Li, Lingwei Tang, Xinhang Wei, Yumin Chen, Haibing Xu Flexible goal‑directed navigation requires integrating changing goal information with a stable spatial map, yet ho...

DeepReinforce Releases Ornith-1.0: An Open-Source Coding Model Family That Learns Its Own RL Scaffolds

DeepReinforce released Ornith-1.0, an open-source coding model family built on Gemma 4 and Qwen 3.5. Instead of a fixed harness, the model learns its own scaffold during reinforcem...

Action Value Functional Variations and Bellman Optimality Fields: Embedding High Speed Q Learning Matrices for Native MQ

Alibaba's model never trained as an agent — and improved agent performance across seven benchmarks

Alibaba's Qwen team released Qwen-AgentWorld on Tuesday — two models trained not to act inside agent environments, but to predict what those environments return. The release covers...

Choosing the Right AI Agent Memory Strategy: A Decision-Tree Approach

In this article, you will learn how to choose the right memory strategy for an AI agent by working through a simple decision tree, one...

Reinforcement Learning Advances Quantum Error Correction Control

In a groundbreaking advancement for quantum computing, researchers have unveiled a novel approach to optimizing quantum error correction (QEC), a critical challenge in maintaining...

A Multi-Agent DDQN Strategic Audit Engine for Silver Markets using Keras/Tensorflow

1. Introduction & Theoretical Framework In modern electronic trading markets, algorithmic execution engines drive the vast majority of institutional order flows. Evaluating whe...

Can Language Models Remember What They Learn?

Post-training methods (RLVR, On-policy distillation) are Episode-local Language models are getting better at learning from feedback during post-training. In reinforcement learning...

Bugcrowd launches Reinforcement Learning environments to help AI models learn real-world security skills

Bugcrowd announced the launch of Reinforcement Learning (RL) Environments, a new offering designed to help AI developers build models that

Humanoid says KinetIQ Ascend reinforcement learning approaches human-level dexterity

Humanoid says its KinetIQ Ascend approach can reach 99.9% manipulation reliability at human speed and beyond for industrial tasks. The post Humanoid says KinetIQ Ascend reinforcem...