タクシー配車における強化学習活用の動向について、DiDi AI Labのアルゴリズムを勉強会用にまとめた資料です。 A survey of reinforcement learning application in taxi dispatching/repositioning domain. The papers are selected mostly from DiDi AI Lab's publications.
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タクシー配車アルゴリズムへの強化学習活用：Reinforcement Learning Applications in Taxi dispatching and repositioning domain
タクシー配車における強化学習活用の動向について、DiDi AI Labのアルゴリズムを勉強会用にまとめた資料です。 A survey of reinforcement learning application in taxi dispatching/repositioning domain. The papers are selected mostly from DiDi AI Lab's publications.
チュートリアル @ 強化学習若手の会 https://young-reinforcement.github.io/ 解説記事（Qiita） https://qiita.com/aiueola/items/90f635200d808f904daf
Faster sorting algorithms discovered using deep reinforcement learning - Nature
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rl-for-llms.md Reinforcement Learning for Language Models Yoav Goldberg, April 2023. Why RL? With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrat
Discovering faster matrix multiplication algorithms with reinforcement learning - Nature
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GitHub - lucidrains/PaLM-rlhf-pytorch: Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM
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Illustrating Reinforcement Learning from Human Feedback (RLHF)
Illustrating Reinforcement Learning from Human Feedback (RLHF) This article has been translated to Chinese 简体中文 and Vietnamese đọc tiếng việt. Language models have shown impressive capabilities in the past few years by generating diverse and compelling text from human input prompts. However, what makes a "good" text is inherently hard to define as it is subjective and context dependent. There are
Pwnagotchi - Deep Reinforcement Learning instrumenting bettercap for WiFi pwning.
navigation Pwnagotchi: Deep Reinforcement Learning for WiFi pwning! Pwnagotchi is an A2C-based “AI” powered by bettercap and running on a Raspberry Pi Zero W that learns from its surrounding WiFi environment in order to maximize the crackable WPA key material it captures (either through passive sniffing or by performing deauthentication and association attacks). This material is collected on disk
ML Platform Meetup: Infra for Contextual Bandits and Reinforcement Learning
Infrastructure for Contextual Bandits and Reinforcement Learning — theme of the ML Platform meetup hosted at Netflix, Los Gatos on Sep 12, 2019. Contextual and Multi-armed Bandits enable faster and adaptive alternatives to traditional A/B Testing. They enable rapid learning and better decision-making for product rollouts. Broadly speaking, these approaches can be seen as a stepping stone to full-o
Learn Intro to Game AI and Reinforcement Learning Tutorials
Practical data skills you can apply immediately: that's what you'll learn in these free micro-courses. They're the fastest (and most fun) way to become a data scientist or improve your current skills.
Introducing Google Research Football: A Novel Reinforcement Learning Environment
Posted by Karol Kurach, Research Lead and Olivier Bachem, Research Scientist, Google Research, Zürich The goal of reinforcement learning (RL) is to train smart agents that can interact with their environment and solve complex tasks, with real-world applications towards robotics, self-driving cars, and more. The rapid progress in this field has been fueled by making agents play games such as the ic
Training a reinforcement learning Agent with Unity and Amazon SageMaker RL | Amazon Web Services
AWS Machine Learning Blog Training a reinforcement learning Agent with Unity and Amazon SageMaker RL Unity is one of the most popular game engines that has been adopted not only for video game development but also by industries such as film and automotive. Unity offers tools to create virtual simulated environments with customizable physics, landscapes, and characters. The Unity Machine Learning A
RLHF（Reinforcement Learning from Human Feedback：人間のフィードバックからの強化学習）とは？
RLHF（Reinforcement Learning from Human Feedback：人間のフィードバックからの強化学習）とは？：AI・機械学習の用語辞典 用語「RLHF」について説明。人間のフィードバックを使ってAIモデルを強化学習する手法を指す。OpenAIのChatGPT／InstructGPTでは、人間の価値基準に沿うように、言語モデルをRLHFでファインチューニング（微調整）している。 連載目次 用語解説 RLHF（Reinforcement Learning from Human Feedback）とは、「人間のフィードバックからの強化学習」という名前の通り、人間の価値基準に沿うように、人間のフィードバックを使ってAI（言語）モデルを強化学習で微調整（ファインチューニング）する手法である。なお強化学習とは、フィードバック（報酬や罰）に基づいて学習する方法のことだ。 R
Welcome to the 🤗 Deep Reinforcement Learning Course - Hugging Face Deep RL Course
Unit 1. Introduction to Deep Reinforcement Learning
Reinforcement Learning Inside Business
kintone Café 大阪 Vol.13 〜karuraで学ぶ、機械学習の活かし方〜Takahiro Kubo2K views•53 slides
Posted by Anna Goldie, Senior Software Engineer and Azalia Mirhoseini, Senior Research Scientist, Google Research, Brain Team Update, June 9, 2021: Today in Nature, we've published methods that improve on what is discussed below, and that have been used in production to design the next generation of Google TPUs. The revolution of modern computing has been largely enabled by remarkable advances in
Large language models (LLMs) have been increasingly used to interact with external environments (e.g., games, compilers, APIs) as goal-driven agents. However, it remains challenging for these language agents to quickly and efficiently learn from trial-and-error as traditional reinforcement learning methods require extensive training samples and expensive model fine-tuning. We propose Reflexion, a
GitHub - google-deepmind/open_spiel: OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games, as well as traditional multiagent environments such as (partia
GitHub - CarperAI/trlx: A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)
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Reinforcement learning is supervised learning on optimized data
Finding good data and a good policy correspond to optimizing the lower bound, $F(\theta, q)$, with respect to the policy parameters and the experience. One common approach for maximizing the lower bound is to perform coordinate ascent on its arguments, alternating between optimizing the data distribution and the policy.1 Optimizing the Policy When optimizing the lower bound with respect to the pol
End-to-End Deep Reinforcement Learning <br> without Reward Engineering
End-to-End Deep Reinforcement Learning without Reward Engineering Communicating the goal of a task to another person is easy: we can use language, show them an image of the desired outcome, point them to a how-to video, or use some combination of all of these. On the other hand, specifying a task to a robot for reinforcement learning requires substantial effort. Most prior work that has applied de
GitHub - DeepX-inc/machina: Control section: Deep Reinforcement Learning framework
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The MIT Press Marc G. Bellemare and Will Dabney and Mark Rowland This textbook aims to provide an introduction to the developing field of distributional reinforcement learning. The book is available at The MIT Press website (including an open access version). The version provided below is a draft. The draft is licensed under a Creative Commons license, see terms and conditions for details. Table o
GitHub - hanjuku-kaso/awesome-offline-rl: An index of algorithms for offline reinforcement learning (offline-rl)
For any questions, feel free to contact: saito@hanjuku-kaso.com Table of Contents Papers Review/Survey/Position Papers Offline RL Off-Policy Evaluation and Learning Related Reviews Offline RL: Theory/Methods Offline RL: Benchmarks/Experiments Offline RL: Applications Off-Policy Evaluation and Learning: Theory/Methods Off-Policy Evaluation: Contextual Bandits Off-Policy Evaluation: Reinforcement Le
Several days ago, AlphaTensor was introduced by DeepMind on Nature. I think this is the third time that DeepMind’s Reinforcement Learning(RL) research hits Nature(AlphaGO, AlphaFold and AlphaTensor now). Although RL is powerful, it is more difficult to jump in because there are fewer resources or systematical resources on this topic. I guess the situation is better now, but this is how I felt when
GitHub - evilsocket/pwnagotchi: (⌐■_■) - Deep Reinforcement Learning instrumenting bettercap for WiFi pwning.
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GitHub - eleurent/phd-bibliography: References on Optimal Control, Reinforcement Learning and Motion Planning
Bibliography Table of contents Optimal Control Dynamic Programming Linear Programming Tree-Based Planning Control Theory Model Predictive Control Safe Control Robust Control Risk-Averse Control Value-Constrained Control State-Constrained Control and Stability Uncertain Dynamical Systems Game Theory Sequential Learning Multi-Armed Bandit Best Arm Identification Black-box Optimization Reinforcement
We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal sequence model. Algorithm Distillation treats learning to reinforcement learn as an across-episode sequential prediction problem. A dataset of learning histories is generated by a source RL algorithm, and then a causal transf
How to implement a Reinforcement Learning library from Scratch — A Deep dive into Reinforce.jl
The goal of this tutorial is to introduce you to Reinforce.jl library which is a Reinforcement Learning library written in Julia by Tom Breloff This is a library written mostly written by a single person and my theory is that Julia is what helps someone smart like Tom be this productive. So we’re gonna be doing some GitHub archaeology and try to figure out how everything in Reinforce.jl fits toget
GitHub - pfnet/pfrl: PFRL: a PyTorch-based deep reinforcement learning library
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AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning
Research AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning Published 30 October 2019 Authors The AlphaStar team TL;DR: AlphaStar is the first AI to reach the top league of a widely popular esport without any game restrictions. This January, a preliminary version of AlphaStar challenged two of the world's top players in StarCraft II, one of the most enduring and
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Offline Reinforcement Learning
Reinforcement Learning for Language Models
Chip Design with Deep Reinforcement Learning
Reflexion: Language Agents with Verbal Reinforcement Learning
Distributional Reinforcement Learning
Best Free Resources to Learn Reinforcement Learning in 2023
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Reinforcement Learning, Fast and Slow
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