  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.
