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

Diluted Near-Optimal Expert Demonstrations for Guiding Dialogue Stochastic Policy Optimisation