"""Dynamic configuration utilities for adaptive training parameters.

This module provides utilities for dynamically adjusting training parameters
during reinforcement learning experiments. It includes functions for adaptive
batch sizing, buffer management, and step-wise parameter adjustment based on
training progress.

Key Components:
    - Dynamic buffer size calculation and management
    - Adaptive batch size adjustment through gradient accumulation
    - Step-wise exponential increase strategies
    - Integration with Ray RLlib training workflows

These utilities are typically used in conjunction with dynamic setup mixins
and callbacks to provide adaptive training behavior that adjusts parameters
based on training progress and computational constraints.
"""
