Datasets:
Tasks:
Reinforcement Learning
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
License:
| 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 | |
| ``` | |