iTharindu/DialogPolicy

Dialog policy optimization for task-oriented conversational agents in low resource setting using Reinforcement learning. The methodology is based on a novel probability based Self-play technique and a novel Reward based sampling technique that prioritizes failed dialogues over successful ones

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