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Dialogue assistants are rapidly becoming an indispensable daily aid. To avoidthe significant effort needed to hand-craft the required dialogue flow, theDialogue Management (DM) module can be cast as a continuous Markov DecisionProcess (MDP) and trained through Reinforcement Learning (RL). Several RLmodels have been investigated over recent years. However, the lack of a commonbenchmarking framework makes it difficult to perform a fair comparison betweendifferent models and their capability to generalise to different environments.Therefore, this paper proposes a set of challenging simulated environments fordialogue model development and evaluation. To provide some baselines, weinvestigate a number of representative parametric algorithms, namely deepreinforcement learning algorithms - DQN, A2C and Natural Actor-Critic andcompare them to a non-parametric model, GP-SARSA. Both the environments andpolicy models are implemented using the publicly available PyDial toolkit andreleased on-line, in order to establish a testbed framework for furtherexperiments and to facilitate experimental reproducibility.

A Benchmarking Environment for Reinforcement Learning Based Task Oriented Dialogue Management