LW Training Platform
Policy Evaluation in Lwlab
Zero-Dependency Isolation
The policy and environment run in completely independent processes with isolated Python environments. This architecture eliminates the notorious "dependency hell" problem
Policy side
can use any deep learning framework with specific versions without conflicts
Environment side
runs Isaac Lab with its required dependencies independently
Rapid iteration
Update policy models without restarting the heavy simulation environment
High-Performance Zero-Copy Communication
The framework implements an optimized inter-process communication (IPC) protocol with shared memory for data transfer
Seamless remote environment access
clients interact with remote environments as if they were local, with transparent API calls
Zero-copy data sharing
via shared memory regions - large observation data (multi-camera RGB-D streams) are transferred without serialization
Sub-millisecond latency
for observation-action loops, enabling real-time policy evaluation with negligible overhead
Flexible Deployment Modes
The distributed design supports multiple deployment paradigms
