Vectorized environments — why and how?
mediumAnswer
- Batch multiple env instances that step in parallel per learner step → wider (batched) but same-length trajectory data.
- Benefits: (1) better GPU utilization for policy inference (batch forward pass over N envs), (2) more diverse data per update, (3) faster wall-clock time.
- Standard: gym.vector.SyncVectorEnv (CPU-bound) or AsyncVectorEnv (parallel workers), OR IsaacGym / Envpool for massively-parallel simulation on GPU.
Check yourself — multiple choice
- One env
- Batch N env instances stepping in parallel → better GPU util (batched policy inference) + diversity + wall-clock speed; SyncVec / AsyncVec / IsaacGym / Envpool
- Random
- Not real
Vec envs: batched parallel envs; GPU util + diversity + speed.
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Practise Reinforcement Learning
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