kengz/SLM-Lab

Health v2 · maintenance only

Adoption not part of the score

1362 stars · 290 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

SLM Lab is a modular deep reinforcement learning framework built in PyTorch, offering ready-to-use algorithms like PPO, SAC, DQN, and A2C validated on 70+ environments. It serves as the companion library to the book 'Foundations of Deep Reinforcement Learning' and includes benchmarking, configuration via JSON specs, and GPU/async training support.

Use cases

• train a PPO agent on CartPole
• run reinforcement learning benchmarks on Atari environments
• learn deep RL by following a book with runnable code
• benchmark SAC on continuous control tasks like HalfCheetah
• run async RL training with Hogwild! on GPU
• compare policy gradient algorithms across environments

When to choose

• you want a modular PyTorch RL framework with validated algorithms
• you are studying deep RL and want code matching a textbook
• you need reproducible RL benchmarks with public data

When to avoid

• you need a production RL deployment platform rather than a research framework
• you work outside Python/PyTorch ecosystems

Facets

framework · maturity active

machine-learning reinforcement-learning benchmarking reinforcement-learning machine-learning deep-learning gaming-tools tutorials python cross-platform pytorch deep-reinforcement-learning ppo sac dqn a2c policy-gradient gymnasium atari benchmarking education companion-book gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="kengz/SLM-Lab")