Migrating from skrl

skrl’s strength is algorithm breadth. UniLab focuses on a curated set (PPO, SAC, TD3 with several optimized variants) but adds a real-hardware deployment path.

Mapping skrl concepts to UniLab

skrl

UniLab

Agent (PPO, SAC, …)

unilab.algos.torch.*

RolloutMemory

unilab.ipc.rollout_ring_buffer

ReplayMemory

unilab.ipc.replay_buffer

Trainer

unilab.training.run

Wrapper for env

NpEnv subclassing

What to expect

  • No algorithm parity for niche algos (CQL, IQL, etc.) — UniLab intentionally focuses on a few highly optimized actor-critic variants.

  • Different runner lifecycle. skrl’s monolithic trainer becomes a collector + learner pair connected by shared memory. See Runner Lifecycle.

  • Different env interface. skrl tolerates many env styles. UniLab insists on NpEnv + dict obs.

Migration checklist

  1. Decide which UniLab algorithm best matches your skrl agent.

  2. Port the env into NpEnv form.

  3. Convert hyperparameter YAML into Hydra groups under conf/<algo>/<task>/.

  4. Validate reward parity.