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"reinforcement learning"に関するエントリは28件あります。 機械学習、 AI、 強化学習 などが関連タグです。 人気エントリには 『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』などがあります。

"reinforcement learning"の関連エントリー

  • 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.

      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

    • Offline Reinforcement Learning

      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

        Offline Reinforcement Learning

      • Faster sorting algorithms discovered using deep reinforcement learning - Nature

        Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

          Faster sorting algorithms discovered using deep reinforcement learning - Nature

        • Reinforcement Learning for Language Models

          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

            Reinforcement Learning for Language Models

            • "reinforcement learning"の関連エントリー

          • Discovering faster matrix multiplication algorithms with reinforcement learning - Nature

            Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

              Discovering faster matrix multiplication algorithms with reinforcement learning - Nature

            • 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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                GitHub - lucidrains/PaLM-rlhf-pytorch: Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM

              • 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

                  Illustrating Reinforcement Learning from Human Feedback (RLHF)

                • 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

                      ML Platform Meetup: Infra for Contextual Bandits and Reinforcement Learning

                    • Learn Intro to Game AI and Reinforcement Learning Tutorials | Kaggle

                      Build your own video game bots, using classic and cutting-edge algorithms.

                      • Reflexion: Language Agents with Verbal Reinforcement Learning

                        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

                        • 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

                            Training a reinforcement learning Agent with Unity and Amazon SageMaker RL | Amazon Web Services

                          • 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

                              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？

                            • Welcome to the 🤗 Deep Reinforcement Learning Course - Hugging Face Deep RL Course

                              Unit 1. Introduction to Deep Reinforcement Learning

                                Welcome to the 🤗 Deep Reinforcement Learning Course - Hugging Face Deep RL Course

                              • Chip Design with Deep Reinforcement Learning

                                Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more

                                  Chip Design with Deep Reinforcement Learning

                                • 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 - google-deepmind/open_spiel: OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.

                                  • GitHub - CarperAI/trlx: A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)

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                                      GitHub - CarperAI/trlx: A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)

                                    • Reinforcement Learning Inside Business

                                      画像認識モデルを自動的に作る。1日以内に。~Simple And Efficient Architecture Search for Convolutio...

                                        Reinforcement Learning Inside Business

                                      • 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

                                          Reinforcement learning is supervised learning on optimized data

                                        • Distributional Reinforcement Learning

                                          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)

                                            Value-Aided Conditional Supervised Learning for Offline RL Jeonghye Kim, Suyoung Lee, Woojun Kim, and Youngchul Sung. arXiv, 2024. Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning Lanqing Li, Hai Zhang, Xinyu Zhang, Shatong Zhu, Junqiao Zhao, and Pheng-Ann Heng. arXiv, 2024. DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Traj

                                              GitHub - hanjuku-kaso/awesome-offline-rl: An index of algorithms for offline reinforcement learning (offline-rl)

                                            • Best Free Resources to Learn Reinforcement Learning in 2023

                                              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

                                                Best Free Resources to Learn Reinforcement Learning in 2023

                                              • GitHub - evilsocket/pwnagotchi: (⌐■_■) - Deep Reinforcement Learning instrumenting bettercap for WiFi pwning.

                                                Pwnagotchi is an A2C-based "AI" leveraging bettercap that learns from its surrounding WiFi environment to maximize the crackable WPA key material it captures (either passively, or by performing authentication and association attacks). This material is collected as PCAP files containing any form of handshake supported by hashcat, including PMKIDs, full and half WPA handshakes. Instead of merely pla

                                                  GitHub - evilsocket/pwnagotchi: (⌐■_■) - Deep Reinforcement Learning instrumenting bettercap for WiFi pwning.

                                                • GitHub - eleurent/phd-bibliography: References on Optimal Control, Reinforcement Learning and Motion Planning

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                                                    GitHub - eleurent/phd-bibliography: References on Optimal Control, Reinforcement Learning and Motion Planning

                                                  • In-context Reinforcement Learning with Algorithm Distillation

                                                    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

                                                        How to implement a Reinforcement Learning library from Scratch — A Deep dive into Reinforce.jl

                                                      • GitHub - pfnet/pfrl: PFRL: a PyTorch-based deep reinforcement learning library

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                                                          GitHub - pfnet/pfrl: PFRL: a PyTorch-based deep reinforcement learning library

                                                        • 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

                                                            AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning
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