Agent57
Agent57 is a model-free distributed reinforcement learning algorithm developed by Google DeepMind and reported in 2020.
Reinforcement Learning
Explore Reinforcement Learning through related topics and the articles other pages reference most.
Explore articles
Browse subtopics: AI Agents
Articles that also belong to these categories. Counts cover all of Reinforcement Learning.
All articles (136)Machine Learning (58)Robotics (22)Deep Learning (12)Google DeepMind (12)People (12)Open Source AI (10)Training & Optimization (10)AI Models (9)AI Research (9)AI in Gaming (8)Algorithms (8)AI Agents (7)Artificial Intelligence (7)Embodied AI (7)Chinese AI (6)OpenAI (6)AI Alignment (5)AI Safety (5)Mathematics (5)World Models (5)AI Companies (4)AI Infrastructure (4)Large Language Models (4)AI Inference (3)Developer Tools (3)NVIDIA (3)AI Benchmarks (2)Computer Vision (2)Meta AI (2)Model Evaluation (2)Reasoning Models (2)Robot Hardware (2)Statistics (2)AI Code Generation (1)AI Hardware (1)Autonomous Vehicles (1)Computer Science (1)Enterprise AI (1)Finance AI (1)Generative AI (1)Google (1)Humanoid Robots (1)Image Generation (1)Multimodal AI (1)Physical AI (1)Research Organizations (1)Robotics Companies (1)
Showing 1-7 of 7 articles
CodeMidas
CodeMidas is an agentic data pipeline from Xiaomi's LLM Core team that turns functionality already implemented in open-source codebases into executable reinforcement learning environments for coding agents.
DeepSeek Elastic Compute (DSec)
DeepSeek Elastic Compute (DSec) is the production sandbox platform that DeepSeek uses to run the isolated execution environments behind agentic reinforcement learning (RL) training and evaluation of its…
Intrinsic Discovery (Induction Labs)
Intrinsic Discovery is an experimental training method introduced by Induction Labs in August 2026.
RAGEN-2
RAGEN-2 is a 2026 research paper and public code extension for diagnosing and mitigating reasoning collapse during reinforcement learning of multi-turn large language model agents.
SPADE (Self-Play in Adaptive Synthetic Executable Environments)
SPADE, short for Self-Play in Adaptive Synthetic Executable Environments, is a reinforcement-learning framework in which one language model alternates between designing executable training environments and…
Tim Rocktäschel
Tim Rocktäschel is a German computer scientist known for his work on reinforcement learning, open-ended learning, and language-based AI agents.