  This paper proposes a novel multiple-input multiple-output (MIMO) symbol
detector that incorporates a deep reinforcement learning (DRL) agent into the
Monte Carlo tree search (MCTS) detection algorithm. We first describe how the
MCTS algorithm, used in many decision-making problems, is applied to the MIMO
detection problem. Then, we introduce a self-designed deep reinforcement
learning agent, consisting of a policy value network and a state value network,
which is trained to detect MIMO symbols. The outputs of the trained networks
are adopted into a modified MCTS detection algorithm to provide useful node
statistics and facilitate enhanced tree search process. The resulted scheme,
termed the DRL-MCTS detector, demonstrates significant improvements over the
original MCTS detection algorithm and exhibits favorable performance compared
to other existing linear and DNN-based detection methods under varying channel
conditions.
