This pure JAX implementation demonstrates foundational knowledge, but careful management of the execution boundary is essential for performance and stability.
- JAX key management exhibits idiomatic, robust use of
jax.random.split for state randomization within the agent interaction. (Quality)
- The imperative Python loops used for rollout collection limit JAX XLA compilation benefits; explore internal vectorization opportunities carefully. (Performance)
- Relying on the internal JAX path
_src.numpy.lax_numpy is extremely brittle and requires immediate refactoring using public APIs. (Stability)
- Mixed NumPy/JAX array handling for environment interaction necessitates defining explicit dtypes like
jnp.float32 for robust XLA compilation. (Efficiency)
- Architecture cleanly separates environment interaction responsibilities (
train.py) from the specialized PPO agent implementation details. (Structure)
