| | --- |
| | license: mit |
| | language: |
| | - en |
| | pretty_name: SLM-Lab Benchmark Results |
| | tags: |
| | - reinforcement-learning |
| | - deep-learning |
| | - pytorch |
| | task_categories: |
| | - reinforcement-learning |
| | --- |
| | |
| | # SLM Lab <br>   |
| |
|
| |
|
| | <p align="center"> |
| | <i>Modular Deep Reinforcement Learning framework in PyTorch.</i> |
| | <br> |
| | <a href="https://slm-lab.gitbook.io/slm-lab/">Documentation</a> · <a href="https://github.com/kengz/SLM-Lab/blob/master/docs/BENCHMARKS.md">Benchmark Results</a> |
| | </p> |
| |
|
| | >**NOTE:** v5.0 updates to Gymnasium, `uv` tooling, and modern dependencies with ARM support - see [CHANGELOG.md](CHANGELOG.md). |
| | > |
| | >Book readers: `git checkout v4.1.1` for *Foundations of Deep Reinforcement Learning* code. |
| |
|
| | ||||| |
| | |:---:|:---:|:---:|:---:| |
| | |  |  |  |  | |
| | | BeamRider | Breakout | KungFuMaster | MsPacman | |
| | |  |  |  |  | |
| | | Pong | Qbert | Seaquest | Sp.Invaders | |
| | |  |  |  |  | |
| | | Ant | HalfCheetah | Hopper | Humanoid | |
| | |  |  |  |  | |
| | | Inv.DoublePendulum | InvertedPendulum | Reacher | Walker | |
| |
|
| | ## Quick Start |
| |
|
| | ```bash |
| | # Install |
| | uv sync |
| | uv tool install --editable . |
| | |
| | # Run demo (PPO CartPole) |
| | slm-lab run # PPO CartPole |
| | slm-lab run --render # with visualization |
| | |
| | # Run custom experiment |
| | slm-lab run spec.json spec_name train # local training |
| | slm-lab run-remote spec.json spec_name train # cloud training (dstack) |
| | |
| | # Help (CLI uses Typer) |
| | slm-lab --help # list all commands |
| | slm-lab run --help # options for run command |
| | |
| | # Troubleshoot: if slm-lab not found, use uv run |
| | uv run slm-lab run |
| | ``` |
| |
|
| | ## Features |
| |
|
| | - **Algorithms**: DQN, DDQN+PER, A2C, PPO, SAC and variants |
| | - **Environments**: Gymnasium (Atari, MuJoCo, Box2D) |
| | - **Networks**: MLP, ConvNet, RNN with flexible architectures |
| | - **Hyperparameter Search**: ASHA scheduler with Ray Tune |
| | - **Cloud Training**: dstack integration with auto HuggingFace sync |
| |
|
| | ## Cloud Training (dstack) |
| |
|
| | Run experiments on cloud GPUs with automatic result sync to HuggingFace. |
| |
|
| | ```bash |
| | # Setup |
| | cp .env.example .env # Add HF_TOKEN |
| | uv tool install dstack # Install dstack CLI |
| | # Configure dstack server - see https://dstack.ai/docs/quickstart |
| | |
| | # Run on cloud |
| | slm-lab run-remote spec.json spec_name train # CPU training (default) |
| | slm-lab run-remote spec.json spec_name search # CPU ASHA search (default) |
| | slm-lab run-remote --gpu spec.json spec_name train # GPU training (for image envs) |
| | |
| | # Sync results |
| | slm-lab pull spec_name # Download from HuggingFace |
| | slm-lab list # List available experiments |
| | ``` |
| |
|
| | Config options in `.dstack/`: `run-gpu-train.yml`, `run-gpu-search.yml`, `run-cpu-train.yml`, `run-cpu-search.yml` |
| |
|
| | ### Minimal Install (Orchestration Only) |
| |
|
| | For a lightweight box that only dispatches dstack runs, syncs results, and generates plots (no local ML training): |
| |
|
| | ```bash |
| | uv sync --no-default-groups |
| | uv run --no-default-groups slm-lab run-remote spec.json spec_name train |
| | uv run --no-default-groups slm-lab pull spec_name |
| | uv run --no-default-groups slm-lab plot -f folder1,folder2 |
| | ``` |
| |
|
| |
|
