Multi-agent deep reinforcement learning (MADRL) meets multi-user MIMO systems

no code implementations • 10 Sep 2021 • Heunchul Lee, Jaeseong Jeong

A multi-agent deep reinforcement learning (MADRL) is a promising approach to challenging problems in wireless environments involving multiple decision-makers (or actors) with high-dimensional continuous action space.

reinforcement-learning Reinforcement Learning (RL)

Deep reinforcement learning approach to MIMO precoding problem: Optimality and Robustness

no code implementations • 30 Jun 2020 • Heunchul Lee, Maksym Girnyk, Jaeseong Jeong

To demonstrate the optimality of the proposed DRL-based precoding framework, we explicitly consider a simple MIMO environment for which the optimal solution can be obtained analytically and show that DQN- and DDPG-based agents can learn the near-optimal policy to map the environment state of MIMO system to a precoder that maximizes the reward function, respectively, in the codebook-based and non-codebook based MIMO precoding systems.

reinforcement-learning Reinforcement Learning (RL)