RL-Driven Adaptive Phase Optimization for IRS-Based Systems

This code simulates an RL-based methodology to dynamically optimize phase shifts within an IRS, aiming to enhance communication quality.

This code simulates a reinforcement learning (RL) strategy for the dynamic optimization of phase shifts in an intelligent reflective surface (IRS) within a wireless communication scenario. Its main goal is the adaptive modification of IRS phase shifts to optimize the signal-to-noise ratio (SNR) at the receiving end, thus improving overall system performance. This code can serve as a foundational framework for exploring the capabilities of RL in more complex and practical IRS optimization scenarios.