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

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 actorcritic. We notably propose a randomised exploration policy which allows for a seamless hybridisation of the learned policy and the expert, which can be seen as a dilution of the expert's demonstration into the resulting policy. Our experiments show that our hybridisation strategy outperforms several baselines, and that it could accelerate the learning when facing real humans.