Mctx
A JAX-native library implementing Monte Carlo tree search algorithms like AlphaZero and MuZero for reinforcement learning research.
What is Mctx?
Mctx is a JAX-native library implementing Monte Carlo tree search (MCTS) algorithms like AlphaZero, MuZero, and Gumbel MuZero. It enables efficient search-based reinforcement learning by combining learned neural network models with parallelizable, JIT-compiled search algorithms that operate on batches of inputs.
Reinforcement learning researchers and practitioners working on search-based agents who need efficient, hackable implementations of MCTS algorithms that integrate with JAX-based neural network models.
Developers choose Mctx because it provides JAX-native implementations that fully support JIT compilation for hardware acceleration while maintaining Python usability, striking a balance between performance and hackability for research exploration. It includes batch-parallel search algorithms designed to work with large learned environment models.
Overview
Monte Carlo tree search in JAX
Use Cases
Best For
- Implementing AlphaZero, MuZero, or Gumbel MuZero algorithms in JAX-based reinforcement learning projects
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