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