Reinforcement Learning in MuJoCo - AI Build & Learn
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About this event
where we pick a new topic and learn by building together.
This event is about training reinforcement learning agents in MuJoCo, the open-source physics engine widely used for robotics and continuous control. We'll set up simulated environments, train policies to control them, and watch the agents actually learn to move.
MuJoCo (Multi-Joint dynamics with Contact) simulates rigid-body physics fast enough to train on, which is why it's a standard RL benchmark. We'll use it through the Gymnasium environments and a training library, tackle classic control tasks (like teaching a simulated robot to walk), and talk through the practical side: reward design, algorithm choice, and how long training actually takes.
Some things to look up to get started:
Tooling:
MuJoCo: https://github.com/google-deepmind/mujoco
Gymnasium MuJoCo environments: https://gymnasium.farama.org/environments/mujoco/
Stable-Baselines3 (RL algorithms): https://github.com/DLR-RM/stable-baselines3
MuJoCo Playground / MJX (GPU-accelerated, JAX): https://github.com/google-deepmind/mujoco_playground
DeepMind Control Suite (dm_control): https://github.com/google-deepmind/dm_control
Resources
- GitHub: https://github.com/sagecodes/ai-build-and-learn
- Events Calendar: https://luma.com/ai-builders-and-learners
- Slack (Discuss during the week): https://slack.flyte.org/
- Hosted by Sage Elliott: https://www.linkedin.com/in/sageelliott/
In this stream
- Intro to topic
- Community Discussion
- Practical examples
Community challenge (optional)
Try spending 30–90 minutes during the week learning or building something related to the topic, then share what you’re working on in Slack.
Note on Flyte / Union
You may see Flyte used in some demos. Flyte is an open-source AI orchestration platform maintained by Union (where I work) for building scalable, durable, and observable AI workflows. You do not need to use Flyte to participate.
- Union: https://www.union.ai/
- Flyte: https://flyte.org/
Drop a comment with ideas for future topics (agents, RAG, MLOps, robotics, frameworks, and more).
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