hyperlax is a unified JAX-based framework for benchmarking and accelerating both classical and quantum reinforcement learning (RL). It provides a high-throughput environment for comparing machine-learning algorithms under identical computational settings, enabling reproducible and fair performance evaluations across model types such as multilayer perceptron (MLP), tensorized neural network and parametrized quantum circuit (PQC). Key features Unified interface for classical, quantum, and tensor-network RL algorithms Batched hyperparameter exploration with vectorized execution Modular configuration system with strongly-typed dataclasses Compatible with HPC clusters through Singularity containers Use cases Comparative benchmarking of classical vs. quantum RL agents Large-scale hyperparameter optimization using Optuna or random/QMC sampling