How to Train Your Robot
The enormous advance in AI capabilities over the last several years has in large part been driven by the availability of vast amounts of training data.
reinforcement-learning
Multimodal reinforcement learning with agentic verifier for AI agents - Microsoft Research
Argos improves multimodal RL by evaluating whether an agent’s reasoning aligns with what it observes over time. The approach reduces visual hallucinations and produces more reliable, data-efficient agents for real-world applications:
Argos is a verification framework for multimodal reinforcement learning
Argos evaluates how those behaviors were produced.
The State of Reinforcement Learning for LLM Reasoning
Understanding GRPO and New Insights from Reasoning Model Papers
the muted response to GPT-4.5 and Llama 4 (non-reasoning) models suggests we are approaching the limits of what scaling model size and data alone can achieve.
A Gentle Introduction to Q-Learning
Get a gentle introduction to one of the most widely used reinforcement learning algorithms to learn optimal courses of action through trial and error.
AI-Driven Antitrust and Competition Law: Algorithmic Collusion, Self-Learning Pricing Tools, and Legal Challenges in the US and EU
AI algorithmic collusion challenges traditional antitrust law. Legal AI frameworks adapt to prosecute self-learning pricing tools
reinforcement learning (RL), can lead to outcomes resembling traditional collusion
antitrust
The State of Reinforcement Learning for LLM Reasoning
A lot has happened this month, especially with the releases of new flagship models like GPT-4.5 and Llama 4. But you might have noticed that reactions to these releases were relatively muted. Why? One reason could be that GPT-4.5 and Llama 4 remain conventional models, which…
What is Q-learning? - Dataconomy
Q-learning is a model-free reinforcement learning algorithm that enables agents to learn optimal actions through interaction with their environment.
GitHub - MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning: This is the homepage of a new book entitled "Mathematical…
This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning." - MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning
(WIP) A Little Bit of Reinforcement Learning from Human Feedback
The Reinforcement Learning from Human Feedback Book
Policy Gradient Methods in Reinforcement Learning
Teaching a Car to Cross a Mountain using Policy Gradient Methods in Python: A Mathematical Deep Dive into Reinforcement Learning
Reinforcement Learning: Training AI Agents Through Rewards and Penalties
Reinforcement learning (RL) is a fascinating field of AI focused on training agents to make decisions by interacting with an environment and learning from rewards and penalties. RL differs from supervised learning because it involves doing rather than learning from a static…
Edge 291: Reinforcement Learning with Human Feedback
1) Reinforcement Learning with Human Feedback(RLHF) 2) The RLHF paper, 3) The transformer reinforcement learning framework.
How reinforcement learning with human feedback is unlocking the power of ge
How reinforcement learning with human feedback helps ensure that businesses are building ethical generative AI models.
Ending an Ugly Chapter in Chip Design
Study tries to settle a bitter disagreement over Google’s chip design AI
Why People Skip Music? On Predicting Music Skips using Deep...
Music recommender systems are an integral part of our daily life. Recent research has seen a significant effort around black-box recommender based approaches such as Deep Reinforcement Learning...
6 Reinforcement Learning Algorithms Explained
Introduction to reinforcement learning terminologies, basics, and concepts (model-free, model-based, online, offline RL)
Hands on introduction to reinforcement learning in Python
One of the biggest barriers to traditional machine learning is that most supervised and unsupervised machine learning algorithms need huge amounts of data to be useful in real world use cases. Even…
Mastering the Game of Stratego with Model-Free Multiagent...
We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level. Stratego is one of the few iconic board...
scikit-and-tensorflow-workbooks/ch16-reinforcement-learning.ipynb at master · bjpcjp/scikit-and-tensorflow-workbooks
based on "Hands-On Machine Learning with Scikit-Learn & TensorFlow" (O'Reilly, Aurelien Geron) - bjpcjp/scikit-and-tensorflow-workbooks
Reinforcement learning: The next great AI tech moving from the lab to the r
Reinforcement learning (RL) is a powerful type of AI technology that can learn strategies to optimally control large, complex systems.
Reinforcement Learning Explained Visually (Part 6): Policy Gradients, step-
A Gentle Guide to the REINFORCE algorithm, in Plain English
N-step Bootstrapping. This is part 7 of the RL tutorial… | by Sagi Shaier |
This is part 7 of the RL tutorial series that will provide an overview of the book “Reinforcement Learning: An Introduction. Second…
Introduction to Various Reinforcement Learning Algorithms - DataScienceCentral.com
This article was written by Steeve Huang. Reinforcement Learning (RL) refers to a kind of Machine Learning method in which the agent receives a delayed reward in the next time step to evaluate its previous action. It was mostly used in games (e.g. Atari, Mario), with performance…
Introduction to Various Reinforcement Learning Algorithms. Part I (Q-Learni
Reinforcement Learning (RL) refers to a kind of Machine Learning method in which the agent receives a delayed reward in the next time step…
Learning to Optimize with Reinforcement Learning
The BAIR Blog
Deep Learning Research Review Week 2: Reinforcement Learning – Adit Deshpan
Engineering at Forward | UCLA CS '19
Deep Reinforcement Learning Demystified (Episode 0)
I recently became interested in learning more about deep reinforcement learning. Recently, big news headlines were made as deep…