Hybrid Reinforcement Learning with Expert State Sequences

Existing imitation learning approaches often require that the complete demonstration data, including sequences of actions and states, are available. In this paper, we consider a more realistic and difficult scenario where a reinforcement learning agent only has access to the state sequences of an expert, while the expert actions are unobserved. We propose a novel tensor-based model to infer the unobserved actions of the expert state sequences. The policy of the agent is then optimized via a hybrid objective combining reinforcement learning and imitation learning. We evaluated our hybrid approach on an illustrative domain and Atari games. The empirical results show that (1) the agents are able to leverage state expert sequences to learn faster than pure reinforcement learning baselines, (2) our tensor-based action inference model is advantageous compared to standard deep neural networks in inferring expert actions, and (3) the hybrid policy optimization objective is robust against noise in expert state sequences.Comment: AAAI 2019; https://github.com/XiaoxiaoGuo/tensor4r

Effect of a combination of Xiaochaihu decoction and teprenone on peripheral blood T lymphocytes in chronic atrophic gastritis, and on expression of COX-2 in gastric mucosa

Purpose: To study the effects of the combined use of Xiaochaihu decoction and teprenone on peripheral blood T lymphocytes in chronic atrophic gastritis (CAG) and gastric mucosal expression of cyclooxygenase-2 (COX-2).Methods: Patients with CAG who were treated at Traditional Chinese Medicine Hospital of Jiaxing from January 2017 to January 2018 were used as subjects. They consisted of observation and control groups (99 patients per group). Both groups were treated with teprenone (50 mg, thrice daily), but patients in the observation group received 200 mL of Xiaochaihu decoction, in addition to teprenone. The treatments were given orally, and lasted for 3 weeks. Comparisons were made between the two groups with respect to the effects of the treatments on peripheral blood T lymphocytes, CAG, and quality of life.Results: Peripheral T lymphocytes, chronic atrophic gastritis and quality of life in patients in the observation group were significantly improved, relative to control group patients. The combined treatment led to a significant decrease in the expression of COX-2. After treatment, there was upregulation in CD3+, CD4+ and CD4+/CD8+ levels in both groups, relative to the corresponding levels prior to drug exposure (p < 0.05). However, the observation group levels of CD3+, CD4+ and CD4+/CD8+ were higher than corresponding control values (t = -14.45, p < 0.001; t = -12.47, p < 0.001; t = -3.49, p < 0.001, respectively). Moreover, results from histopathological studies showed marked improvement in the observation group.Conclusion: A combination treatment of Xiaochaihu decoction and teprenone improves the condition of CAG patients via changes in peripheral blood lymphocytes and COX-2 expression in gastric mucosa.Keywords: Xiaochaihu decoction, Teprenone, Chronic atrophic gastritis, T lymphocytes, Cyclooxygenase-

One-Shot Relational Learning for Knowledge Graphs

Knowledge graphs (KGs) are the key components of various natural language processing applications. To further expand KGs' coverage, previous studies on knowledge graph completion usually require a large number of training instances for each relation. However, we observe that long-tail relations are actually more common in KGs and those newly added relations often do not have many known triples for training. In this work, we aim at predicting new facts under a challenging setting where only one training instance is available. We propose a one-shot relational learning framework, which utilizes the knowledge extracted by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures. Empirically, our model yields considerable performance improvements over existing embedding models, and also eliminates the need of re-training the embedding models when dealing with newly added relations.Comment: EMNLP 201