200 research outputs found
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
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
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