agi-brain/xuance

Health v2 · maintenance only

Adoption not part of the score

1082 stars · 160 forks observed · 2026-09-03

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

XuanCe is an open-source Python library of deep reinforcement learning (DRL) and multi-agent reinforcement learning (MARL) algorithm implementations, supporting PyTorch, TensorFlow, and MindSpore backends. It includes a wide range of algorithms (DQN variants, PPO, SAC, QMIX, MAPPO, model-based and offline RL) with benchmark tooling for environments like Atari, MuJoCo, SMAC, and Google Research Football.

Use cases

• train deep reinforcement learning agents in Python
• run multi-agent RL experiments like QMIX or MAPPO on StarCraft II
• benchmark DRL algorithms on Atari and MuJoCo environments
• implement custom RL algorithms with a unified API
• switch between PyTorch, TensorFlow, and MindSpore backends for RL research
• experiment with offline and model-based reinforcement learning
• train agents in PettingZoo multi-agent environments

When to choose

• you need a broad zoo of single-agent and multi-agent RL algorithms in one library
• you want backend flexibility across PyTorch, TensorFlow, and MindSpore
• you need reproducible benchmarking against standard RL environments
• you are doing RL research and want readable, extensible implementations

When to avoid

• you need a production RL serving or deployment system rather than a research library
• you only need a single specific algorithm with minimal dependencies
• you require non-Python or real-time embedded RL support

Facets

library · maturity active

machine-learning reinforcement-learning deep-learning benchmarking sdk reinforcement-learning machine-learning deep-learning artificial-intelligence python cross-platform reinforcement-learning-library multi-agent-reinforcement-learning marl pytorch tensorflow mindspore gymnasium pettingzoo atari mujoco starcraft2 ppo qmix maddpg offline-rl model-based-rl algorithms research gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="agi-brain/xuance")