Publications

In a single-agent setting, reinforcement learning (RL) tasks can be cast into an inference problem by introducing a binary random …

We propose a new reasoning protocol called generalized recursive reasoning (GR2), and embed it into the multi-agent reinforcement …

In this paper, we introduce a probabilistic recursive reasoning (PR2) framework for multi-agent reinforcement learning. Our hypothesis …

We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by …

In typical reinforcement learning (RL), the environment is assumed given and the goal of the learning is to identify an optimal policy …

Many artificial intelligence (AI) applications often require multiple intelligent agents to work in a collaborative effort. Efficient …

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage inmany Web applications …

Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this …