Reinforcement Learning 101 - Robotics Simulation RL: 7th out of 7 Sessions
Join our 1hour/wk x 6wk, Reinforcement Learning 101 study group, from basics to RLHF, World Model, and MiniMax & Others‘ RL-for-agents Framework, and coding up RL models, with our community friend teacher Colby (Ziyu) Wang (RL class TA in TMU) and 100+ registered classmates. Open to developer, ML engineer, and ML researchers.
Reinforcement learning matters because it helps AI agents make decisions in robotics, games, healthcare, and recommendation systems, and could be an important building block on the path toward AGI.
Also sharing the overwhelming feedbacks received during the session:
“This presentation was amazing, magnificent.” — Miris
“Thank you for a very nice presentation.” — Sachin
“Great presentation.” — Blush
Just finished 1st session last wk. Feel free to watch the recording: https://lnkd.in/etCkCThs
And watch the future recording in our community “Paper Reading Group” Section: https://lnkd.in/enYhHk_4
Register for next wks class at: https://lnkd.in/gpGQExxe
Code repo for the class: https://lnkd.in/eERpA8ju
Full Official Agenda (6 Core + 1 Extra Session)
Week 1 — Apr 4: Introduction to reinforcement learning — how AI learns through trial and error.
Week 2 — Apr 11: Value + Policy based RL — how AI scores actions and learns to choose better actions.
Week 3 — Apr 18: Value & Policy Hybrid RL — how AI combines scoring and action-taking.
Week 4 — Apr 25: RL in Agent — how MiniMax Forge trains agents for complex tasks.
Week 5 — May 2: RLHF — how human feedback helps AI become more helpful.
Week 6 — May 9: World Model RL — how AI imagines outcomes before taking action.
Extra Special Week 7 — May 17: Robotics Simulation RL (Isaac Sim & MuJoCo)
Core content: Core concept intro, use cases, workflow & practical application of Isaac Sim and MuJoCo for robot reinforcement learning training and physical simulation.
Who is this for?
RL beginners seeking systematic and in-depth learning
Learners with basic coding ability willing to follow practical tutorials
ML practitioners wanting to link RL with LLMs, world models and physical robot simulation
Logistics
Format: Zoom
Core Course: 1 hour per week, 6 weeks total
Extra Session: Independent exclusive extended lecture
Time: 12:00 AM - 1:15 AM GMT+8
If you focus on decision-making AI, agent development and robotic RL practice, welcome to join our learning community.
About AI Scholars
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