In knowledge graph completion (KGC) and other applications, learning how to move from a source node to a target node with a given query is an important problem. It can be formulated as a reinforcement learning (RL) problem transition model under a given state. In order to overcome the challenges of sparse rewards and historical state encoding, we develop a deep agent network (graph-agent, GA), which combines temporal convolutional network (TCN) and Monte Carlo Tree Search (MCTS). Firstly, we

more »

... bine MCTS with neural network to generate more positive reward trajectories, which can effectively solve the problem of sparse rewards. TCN is used to encode the history state, which is used for policy and Q-value respectively. Secondly, according to these trajectories, we use Q-Learning to improve the network and parameter sharing to enhance TCN strategy. We apply these steps repeatedly to learn the model. Thirdly, in the prediction stage of the model, Monte Carlo Tree Search combined with Q-value method is used to predict the target nodes. The experimental results on several graph-walking benchmarks show that GA is better than other RL methods based on-policy gradient. The performance of GA is also better than the traditional KGC baselines.