FlashSAC

FlashSAC runs through src/unilab/scripts/train_flashsac.py in its own config tree. Select it with --algo flashsac; defaults are inlined in src/unilab/conf/flashsac/config.yaml, and the implementation lives under uni_rl.algos.flash_sac (unilab-rl repo).

It shares the off-policy runner design with SAC and TD3, but does not use the same default networks: the actor uses a block-based structure and the critic uses a distributional (categorical) Q variant.

Quick Start

uv run train --algo flashsac --task g1_walk_flat --sim mujoco
uv run train --algo flashsac --task go2_joystick_flat --sim mujoco training.no_play=true

Key Fields

For the off-policy playback path (src/unilab/scripts/train_flashsac.py / CLI --algo flashsac), set training.export_onnx=false to skip policy.onnx export while still recording playback video. See Evaluation and Playback.

  • algo.algo_log_name=flash_sac

  • algo.num_envs=1024

  • algo.max_iterations=5000

  • algo.tau=0.01

  • algo.save_interval=1000

  • algo.algo_params.actor_num_blocks=2

  • algo.algo_params.critic_num_blocks=2

FlashSAC requires synchronized collection and the same sole replay path as SAC and TD3: bounded host ingress plus one complete replay ring on a CUDA or Apple MPS learner device. CPU and XPU training are unsupported.

The log root is logs/flash_sac/<task>/.