---
license: mit
language:
- en
pretty_name: SLM-Lab Benchmark Results
tags:
- reinforcement-learning
- deep-learning
- pytorch
task_categories:
- reinforcement-learning
---

# SLM Lab <br> ![GitHub tag (latest SemVer)](https://img.shields.io/github/tag/kengz/slm-lab) ![CI](https://github.com/kengz/SLM-Lab/workflows/CI/badge.svg)


<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.

|||||
|:---:|:---:|:---:|:---:|
| ![ppo beamrider](https://user-images.githubusercontent.com/8209263/63994698-689ecf00-caaa-11e9-991f-0a5e9c2f5804.gif) | ![ppo breakout](https://user-images.githubusercontent.com/8209263/63994695-650b4800-caaa-11e9-9982-2462738caa45.gif) | ![ppo kungfumaster](https://user-images.githubusercontent.com/8209263/63994690-60469400-caaa-11e9-9093-b1cd38cee5ae.gif) | ![ppo mspacman](https://user-images.githubusercontent.com/8209263/63994685-5cb30d00-caaa-11e9-8f35-78e29a7d60f5.gif) |
| BeamRider | Breakout | KungFuMaster | MsPacman |
| ![ppo pong](https://user-images.githubusercontent.com/8209263/63994680-59b81c80-caaa-11e9-9253-ed98370351cd.gif) | ![ppo qbert](https://user-images.githubusercontent.com/8209263/63994672-54f36880-caaa-11e9-9757-7780725b53af.gif) | ![ppo seaquest](https://user-images.githubusercontent.com/8209263/63994665-4dcc5a80-caaa-11e9-80bf-c21db818115b.gif) | ![ppo spaceinvaders](https://user-images.githubusercontent.com/8209263/63994624-15c51780-caaa-11e9-9c9a-854d3ce9066d.gif) |
| Pong | Qbert | Seaquest | Sp.Invaders |
| ![sac ant](https://user-images.githubusercontent.com/8209263/63994867-ff6b8b80-caaa-11e9-971e-2fac1cddcbac.gif) | ![sac halfcheetah](https://user-images.githubusercontent.com/8209263/63994869-01354f00-caab-11e9-8e11-3893d2c2419d.gif) | ![sac hopper](https://user-images.githubusercontent.com/8209263/63994871-0397a900-caab-11e9-9566-4ca23c54b2d4.gif) | ![sac humanoid](https://user-images.githubusercontent.com/8209263/63994883-0befe400-caab-11e9-9bcc-c30c885aad73.gif) |
| Ant | HalfCheetah | Hopper | Humanoid |
| ![sac doublependulum](https://user-images.githubusercontent.com/8209263/63994879-07c3c680-caab-11e9-974c-06cdd25bfd68.gif) | ![sac pendulum](https://user-images.githubusercontent.com/8209263/63994880-085c5d00-caab-11e9-850d-049401540e3b.gif) | ![sac reacher](https://user-images.githubusercontent.com/8209263/63994881-098d8a00-caab-11e9-8e19-a3b32d601b10.gif) | ![sac walker](https://user-images.githubusercontent.com/8209263/63994882-0abeb700-caab-11e9-9e19-b59dc5c43393.gif) |
| 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
```