  A learning dialogue agent can infer its behaviour from interactions with the
users. These interactions can be taken from either human-to-human or
human-machine conversations. However, human interactions are scarce and costly,
making learning from few interactions essential. One solution to speedup the
learning process is to guide the agent's exploration with the help of an
expert. We present in this paper several imitation learning strategies for
dialogue policy where the guiding expert is a near-optimal handcrafted policy.
We incorporate these strategies with state-of-the-art reinforcement learning
methods based on Q-learning and actor-critic. We notably propose a randomised
exploration policy which allows for a seamless hybridisation of the learned
policy and the expert. Our experiments show that our hybridisation strategy
outperforms several baselines, and that it can accelerate the learning when
facing real humans.
