Browser demo of TAMER makes human-feedback RL policy changes tangible
PeterStone_TX · x · 2026-09-10
This retweet discusses a browser-based demo of TAMER, the classic RL framework for training agents from human feedback: you can watch a policy change in real time as human feedback is applied. The commenter finds it a clean way to build intuition about human-feedback shaping before adding the messiness of robot state and delayed credit assignment. Useful for teaching and getting started with human-in-the-loop RL.
More from Research
- Gensyn builds IR3DE-AXL, a decentralized collective inference network with no central gateway — benfielding · 2026-09-10
- If AI solves a Millennium Prize problem using human research, who gets credit? — Egologic · 2026-09-10
- Pretraining study: varied auxiliary views beat repetition for LLM knowledge acquisition — kastnerkyle · 2026-09-10
- ByteDance Seed Unveils ByteWrist: A Parallel Robotic Wrist for Confined-Space Manipulation — scott_e_reed · 2026-09-10
- The Prism Hypothesis unified autoencoding paper accepted at ECCV 2026, paves way for encoder-free MLLMs — liuziwei7 · 2026-09-10
- embedflow Migrates Embedding Models Without Re-embedding: Qwen 4B→8B Matches Native Retrieval with Just 50 Reranked Docs — Potential_Low_1183 · 2026-09-10