SLM-Lab/benchmark

SLM Lab

Modular Deep Reinforcement Learning framework in PyTorch.
Documentation · Benchmark Results

NOTE: v5.0 updates to Gymnasium, uv tooling, and modern dependencies with ARM support - see 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

# 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.

# 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):

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