AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning
TABLE III: Reward and success rates after 1000/4000 training dialogues. The results in bold blue are the best success rates, and the results in bold black are the best rewards.
['[EMPTY]', '[EMPTY]', 'Baselines GP-Sarsa', 'Baselines GP-Sarsa', 'Baselines DQN', 'Baselines DQN', 'Baselines Feudal-DQN', 'Baselines Feudal-DQN', 'Structured DRL FM-GNN', 'Structured DRL FM-GNN', 'Structured DRL UM-DGNN', 'Structured DRL UM-DGNN', 'Structured DRL MM-DGNN', 'Structured DRL MM-DGNN', 'Structured DRL FX-DGNN', 'Structured DRL FX-DGNN', 'Structured DRL FM-DGNN', 'Structured DRL FM-DGNN']
In this subsection, we compare the proposed AgentGraph models (FM-DGNN and FM-GNN) with traditional RL models (GP-Sarsa and DQN) The learning curves of success rates and reward are shown in Fig. We find that the average performance of FM-DGNN is better than that of Feudal-DQN after both 1000 and 4000 training dialogues. In some tasks (e.g. SFR-Env.1 and LAP-Env.1), the performance of Feudal-DQN is rather low. However, here FM-DGNN doesn’t suffer from this problem.