Jumanji
A diverse suite of scalable reinforcement learning environments written in JAX for hardware-accelerated research.
What is Jumanji?
Jumanji is a suite of reinforcement learning environments written in JAX, providing a diverse collection of scalable simulations for training and evaluating RL agents. It solves the problem of slow environment iteration by leveraging JAX's hardware acceleration and parallelization capabilities, enabling researchers to conduct large-scale experiments efficiently.
Reinforcement learning researchers and practitioners who need fast, scalable environments for experimentation, particularly those working with JAX-based machine learning stacks.
Developers choose Jumanji for its high-speed, JAX-native environments that support automatic vectorization and JIT compilation, combined with a diverse range of problems from simple games to NP-hard combinatorial challenges.
Overview
🕹️ A diverse suite of scalable reinforcement learning environments in JAX
Use Cases
Best For
- Training RL agents on combinatorial optimization problems like vehicle routing or job scheduling
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