gradio — Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
Pythonでマシンラーニングアプリを作成・共有することができるライブラリです。
マシンラーニングアプリ作成
MLINFO COLLECTION
強化学習
記事一覧
1 / 9MaaAssistantArknights — 《明日方舟》小助手，全日常一键长草！| A one-click tool for the daily tasks of Arknights, supporting all clients.
ゲーム『明日方舟』の支援ツール。全日常のタスクを一括で実行可能。
ゲームの支援ツール
qlib — Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.
クエンティング投資プラットフォームを実現するためにAI技術を活用します。
クエンティング投資プラットフォーム
Gymnasium — A standard API for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
Gymnasiumは、シングルエージェントRLの疑似環境を提供するAPIです。
疑似環境を提供する
machine-learning-for-trading — Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
LLMの推論 Transparency を高めるために、DiffusionGemmaの計算を分離しVariable Transparency とAlgorithmic Transparencyを評価します。
LLMの透明性、誤用、過度安定化を理解する
world2vec: Mechanistic world models through latent dynamics learning
World models can achieve strong control performance without faithfully capturing the dynamics of their environment, limiting their potential for mechanistic interpretability and scientific discovery.…
reinforcement-learning
Probing Spatial State Representations in Visual Foundation Models
An observer sees only part of the surrounding world, yet must keep track of what lies beyond the current view. Spatial perception therefore involves more than recognizing individual images: it calls…
reinforcement-learning
A Conveyor Belt Mechanism for Repositionable Magnetic Coupling Between Continuum Robots
This work proposes a novel conveyor belt mechanism enabling continuous, active repositioning of functional elements along the body of a tendon-driven continuum robot. Up to three conveyor belt…
reinforcement-learning
PIVOT: Progressive Intervention via Online Teacher Correction
Reinforcement learning from verifiable rewards (RLVR) is limited by sparse outcome-level feedback on hard reasoning tasks: when all rollouts in a group fail, rewards provide no learning signal.…
reinforcement-learning
Training language models in simulation for psychiatric intake
In psychiatric practice, the clinical intake interview remains a primary diagnostic instrument, requiring the clinician to establish rapport while disentangling complex, overlapping presentations.…
reinforcement-learning
Optimal Path Planning with Localization constraints
This paper studies UAV path planning in areas where GPS signals are not present. Because these drones can easily get lost when they don't have GPS, they must rely on dead reckoning and occasionally…
reinforcement-learning
Bridging Neural Representations and Symbolic Semantics in Audio Classification
Deep audio classifiers can achieve strong predictive performance, but their hidden representations remain difficult to interpret. Existing audio explainability methods often focus on input-level…
reinforcement-learning
Securing the Robotics Supply Chain
This paper sets out what a sustained federal research and investment agenda would need to include to close that gap--not within a single budget cycle, but over the decade such an undertaking will…
reinforcement-learning
The generative paradox: the Anti-Prompt paradigm for human capability
Generative AI in education is usually judged by the output of human-AI teams. That focus overlooks a second outcome: what people can still understand, challenge, repair, and adapt when assistance…
reinforcement-learning
Cheap Retrieval and Observation Credit Assignment from One Quantity: The Value of Information
Memory in reinforcement learning raises two problems. Attention finds the relevant item by scoring the whole store, which is costly for robots under latency and energy limits.
reinforcement-learning
Human--AI Interactions as First-Class Data: A Formal Interaction Model and Runtime for Explainable AI Collaboration
AI assistants are increasingly embedded in computational notebooks, scientific workflows, software engineering tools, and learning systems, yet human-AI interactions are often treated as transient…
reinforcement-learning
Learning Calibrated Confidence for Multi-Agent Collaboration
Complex information seeking on the Web often requires collecting evidence from multiple documents and reasoning across them, as in multi-hop question answering. Multi-agent systems address such tasks…
reinforcement-learning
Deep Reinforcement Learning for Blind Passive Beamforming in RIS-Assisted MIMO Systems
Reconfigurable Intelligent Surfaces (RIS) are a promising solution for improving millimeter-wave (mmWave) communications by controlling signal propagation. Yet, selecting the optimal RIS…
reinforcement-learning
PASA-KV: Attacking Action-Facing Visual Routes in World Action Models
World action models (WAMs) increasingly couple internal visual or world representations with action generation, making their world-to-action pathways a critical yet poorly understood source of…
reinforcement-learning
PAPERBENCHX: Can AI Reproduce Science Across Disciplines?
Before AI can be trusted to discover new science, it should be able to reproduce what science already knows. Reproduction provides a rigorous checkpoint for an AI scientist: the target claim is…
reinforcement-learning
Bank-World: A Benchmark for Evaluating Agents on Banking Workflows
Bank-World is a benchmark of 50 banking tasks across two simulated institutions. Completing banking work requires more than producing the right calculation: agents must reconcile records, obtain…
reinforcement-learning
Adaptive Federated Optimization Across Smooth and Nonsmooth Regimes
AdaGrad-style methods can simultaneously adapt to both the order and constants of smoothness in centralized optimization. However, existing extensions to federated optimization with heterogeneous…
reinforcement-learning
Kinematic-Tactile Aligned Dexterous Retargeting with Contact Wrench Space Alignment
Human demonstrations provide a natural and scalable source of data for learning dexterous manipulation policies. However, existing retargeting methods primarily transfer human kinematics, while the…
reinforcement-learning
Hidden Phase Transition in Single-Polymer Stretching under Elongational Flow
Using machine learning, we identify a second transition in polymer chain stretching driven by planar elongational flow, beyond the well-known coil-stretch transition. We achieve this by developing a…
reinforcement-learning