One of the key enablers of the ChatGPT magic can be traced back to 2017 under the obscure name of reinforcement learning with human feedback(RLHF).
Large language models(LLMs) have become one of the most interesting environments for applying modern reinforcement learning(RL) techniques. While LLMs are great at deriving knowledge from vast amounts of text, RL can help to translate that knowledge into actions. That has been the secret behind RLHF.
Hi Geeks, welcome to Part-3 of our Reinforcement Learning Series. In the last two blogs, we covered some basic concepts in RL and also studied the multi-armed bandit problem and its solution methods…
When the agent interacts with the environment, the sequence of experienced tuples can be highly correlated. The naive Q-Learning algorithm that learns from each of these experience tuples in…
In Q-Learning, we represent the Q-value as a table. However, in many real-world problems, there are enormous state and/or action spaces and tabular representation is insufficient. For instance…
This is a PyTorch implementation/tutorial of Deep Q Networks (DQN) from paper Playing Atari with Deep Reinforcement Learning. This includes dueling network architecture, a prioritized replay buffer and double-Q-network training.
In this article, we will try to understand where On-Policy learning, Off-policy learning and offline learning algorithms fundamentally differ. Though there is a fair amount of intimidating jargon in…
A research team from McGill University, Université de Montréal, DeepMind and Mila presents an end-to-end, model-based deep reinforcement learning (RL) agent that dynamically attends to relevant parts of its environments to facilitate out-of-distribution (OOD) and systematic generalization.
A paper by DeepMind scientist triggered much debate about the path to artificial intelligence. Here, we'll try to draw the line between theory and practice.
- Sep. 28 – Oct. 2, 2020
- Lihong Li (Google Brain; chair), Marc G. Bellemare (Google Brain)
- The success of deep neural networks in modeling complicated functions has recently been applied by the reinforcement learning community, resulting in algorithms that are able to learn in environments previously thought to be much too large. Successful applications span domains from robotics to health care. However, the success is not well understood from a theoretical perspective. What are the modeling choices necessary for good performance, and how does the flexibility of deep neural nets help learning? This workshop will connect practitioners to theoreticians with the goal of understanding the most impactful modeling decisions and the properties of deep neural networks that make them so successful. Specifically, we will study the ability of deep neural nets to approximate in the context of reinforcement learning.
- Aug. 31 – Sep. 4, 2020
- Csaba Szepesvari (University of Alberta, Google DeepMind; chair), Emma Brunskill (Stanford University), Sébastien Bubeck (MSR), Alan Malek (DeepMind), Sean Meyn (University of Florida), Ambuj Tewari (University of Michigan), Mengdi Wang (Princeton)
This program aims to reunite researchers across disciplines that have played a role in developing the theory of reinforcement learning. It will review past developments and identify promising directions of research, with an emphasis on addressing existing open problems, ranging from the design of efficient, scalable algorithms for exploration to how to control learning and planning. It also aims to deepen the understanding of model-free vs. model-based learning and control, and the design of efficient methods to exploit structure and adapt to easier environments.
Learn AI from Stanford professors Christopher Manning, Andrew Ng, and Emma Brunskill. Free online course videos in Deep Learning, Reinforcement Learning, and Natural Language Processing.
The purpose of AI Magazine is to disseminate timely and informative articles that represent the current state of the art in AI and to keep its readers posted on AAAI-related matters. The articles are selected for appeal to readers engaged in research and
This is CMSC389F, the University of Maryland's theoretical introduction to the art of reinforcement learning. An introductory course taught by Kevin Chen and Zack Khan, CMSC389F covers topics including markov decision processes, monte carlo methods, policy gradient methods, exploration, and application towards real environments in broad strokes .
In Model-based Reinforcement Learning, Generative And Temporal Models Of Environments Can Be Leveraged To Boost Agent Performance, Either By Tuning The Agent's Representations During Training Or Via Use As Part Of An Explicit Planning Mechanism. However, Their Application In Practice Has Been Limited To Simplistic Environments, Due To The Difficulty Of Training Such Models In Larger, Potentially Partially-observed And 3d Environments. In This Work We Introduce A Novel Action-conditioned Generative Model Of Such Challenging Environments. The Model Features A Non-parametric Spatial Memory System In Which We Store Learned, Disentangled Representations Of The Environment. Low-dimensional Spatial Updates Are Computed Using A State-space Model That Makes Use Of Knowledge On The Prior Dynamics Of The Moving Agent, And High-dimensional Visual Observations Are Modelled With A Variational Auto-encoder. The Result Is A Scalable Architecture Capable Of Performing Coherent Predictions Over Hundreds Of Time Steps Across A Range Of Partially Observed 2d And 3d Environments.
Through my PhD on Deep Learning based robotics, I read a lot of papers on Machine Learning, Reinforcement Learning and AI in general. But papers can be a bit...
Introduction to Reinforcement Learning, including a definition, analysis of the motivations and limitations of AI, and an overview of the technology along with its applications.
Asynchronous methods for deep reinforcement learning Mnih et al. ICML 2016 You know something interesting is going on when you see a scalability plot that looks like this: That’s a superlinear speedup as we increase the number of threads, giving a 24x performance improvement with 16 threads as compared to a single thread. The result…
The codebase contains a replica of the AlphaZero methodology, built in Python and Keras. Gain a deeper understanding of how AlphaZero works and adapt the code to plug in new games.
We introduce AlphaGo Zero, the latest evolution of AlphaGo, the first computer program to defeat a world champion at the ancient Chinese game of Go. Zero is even more powerful and is arguably the strongest Go player in history. Previous versions of AlphaGo initially trained on thousands of human amateur and professional games to learn how to play Go. AlphaGo Zero skips this step and learns to play simply by playing games against itself, starting from completely random play. In doing so, it quickly surpassed human level of play and defeated the previously published champion-defeating version of AlphaGo by 100 games to 0.
These are lectures for course 6.S094: Deep Learning for Self-Driving Cars taught in Winter 2017. Course website: http://cars.mit.edu Contact: deepcars@mit.ed...
I have been working on Reinforcement Learning for the past few months and all I can say about it: It is different. A writeup of the common quirks and frustrations of Reinforcement Learning I have…
Humans excel at solving a wide variety of challenging problems, from low-level motor control through to high-level cognitive tasks. Our goal at DeepMind is to create artificial agents that can achieve a similar level of performance and generality. Like a human, our agents learn for themselves to achieve successful strategies that lead to the greatest long-term rewards.
PyBrain is a modular Machine Learning Library for Python. Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms.
PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library.
Y. Chen, and M. Bansal. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), page 675--686. Melbourne, Australia, Association for Computational Linguistics, (July 2018)