  The combination of multiple-input multiple-output (MIMO) systems and
intelligent reflecting surfaces (IRSs) is foreseen as a critical enabler of
beyond 5G (B5G) and 6G. In this work, two different approaches are considered
for the joint optimization of the IRS phase-shift matrix and MIMO precoders of
an IRS-assisted multi-stream (MS) multi-user MIMO (MU-MIMO) system. Both
approaches aim to maximize the system sum-rate for every channel realization.
The first proposed solution is a novel contextual bandit (CB) framework with
continuous state and action spaces called deep contextual bandit-oriented deep
deterministic policy gradient (DCB-DDPG). The second is an innovative deep
reinforcement learning (DRL) formulation where the states, actions, and rewards
are selected such that the Markov decision process (MDP) property of
reinforcement learning (RL) is appropriately met. Both proposals perform
remarkably better than state-of-the-art heuristic methods in scenarios with
high multi-user interference.
