opendilab/DI-engine

Health v2 · maintenance only

Adoption not part of the score

3638 stars · 437 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

DI-engine is an open-source reinforcement learning framework from OpenDILab that provides comprehensive implementations of deep RL algorithms built on PyTorch. It supports single-agent, multi-agent, offline, and model-based RL with distributed training capabilities across many benchmark environments.

Use cases

• train reinforcement learning agents in Python
• run multi-agent RL experiments with self-play
• implement imitation learning and inverse RL
• train offline RL policies from datasets
• benchmark RL algorithms on Atari, MuJoCo, and SMAC
• run distributed RL training like IMPALA

When to choose

• you need a broad collection of ready-to-use RL algorithms in PyTorch
• you want distributed or multi-agent RL training support
• you need offline RL or imitation learning out of the box

When to avoid

• you need a lightweight single-algorithm library with minimal dependencies
• you work outside Python or PyTorch ecosystems
• you only need simple supervised deep learning

Facets

framework · maturity active

reinforcement-learning machine-learning deep-learning agent-framework simulation reinforcement-learning machine-learning artificial-intelligence python pytorch multi-agent-rl offline-rl imitation-learning self-play distributed-rl decision-ai ai-agents game-development linux macos gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="opendilab/DI-engine")