Skip to yearly menu bar Skip to main content


Poster

M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search

Yelong Shen · Jianshu Chen · Po-Sen Huang · Yuqing Guo · Jianfeng Gao

Room 517 AB #164

Keywords: [ Reinforcement Learning and Planning ] [ Reinforcement Learning ] [ Model-Based RL ] [ Planning ]


Abstract:

Learning to walk over a graph towards a target node for a given query and a source node is an important problem in applications such as knowledge base completion (KBC). It can be formulated as a reinforcement learning (RL) problem with a known state transition model. To overcome the challenge of sparse rewards, we develop a graph-walking agent called M-Walk, which consists of a deep recurrent neural network (RNN) and Monte Carlo Tree Search (MCTS). The RNN encodes the state (i.e., history of the walked path) and maps it separately to a policy and Q-values. In order to effectively train the agent from sparse rewards, we combine MCTS with the neural policy to generate trajectories yielding more positive rewards. From these trajectories, the network is improved in an off-policy manner using Q-learning, which modifies the RNN policy via parameter sharing. Our proposed RL algorithm repeatedly applies this policy-improvement step to learn the model. At test time, MCTS is combined with the neural policy to predict the target node. Experimental results on several graph-walking benchmarks show that M-Walk is able to learn better policies than other RL-based methods, which are mainly based on policy gradients. M-Walk also outperforms traditional KBC baselines.

Live content is unavailable. Log in and register to view live content