Self-Play Training for Task-Oriented Dialog Agents
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Solution Overview
Problem
Training automatic agents to interact with humans in a human-like manner is challenging, especially when direct interaction with the real-world environment is impractical and costly.
Innovation Solution
A self-training method using reinforcement learning where a simulated user provides secret tasks to an automatic agent, allowing the agent to learn through reinforcement policies, rewards, and penalties in a simulated environment, enabling it to generate actions and adjust policies to complete tasks effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automatic agents are trained by directly interacting with the real-world environment, then the agents can learn human-like interaction strategies, but the training cost and practical limitations increase significantly
Solution Approach 1:
The patent creates a simulated environment that copies the essential characteristics of the real-world environment. Instead of training agents by having them directly interact with humans in real conversations, the system uses simulated users that replicate human interaction patterns. This allows agents to learn human-like interaction strategies through repeated practice in the simulation without incurring the high costs of real-world training.
Solution Approach 2:
The patent introduces simulated users as intermediaries between the training system and real human interactions. These simulated users act as mediators that provide training feedback without requiring actual human involvement during the training process. The simulated users can be programmed to follow specific policies and provide consistent, scalable training data while avoiding the practical limitations of direct human interaction.
2Productivity
If automatic agents are trained using simulated users with secret tasks, then training efficiency improves, but the complexity of the training system increases
Solution Approach 1:
The patent segments the training system into distinct modular components: simulated users with secret tasks, automatic agents being trained, reward determination modules, and policy adjustment mechanisms. Each component has a specific function and can be independently configured and modified. This segmentation allows for efficient training by enabling parallel processing of multiple training scenarios while managing system complexity through clear separation of concerns.
Data Source
AI summary
An automatic agent may be trained using reinforcement learning. A secret task may be obtained for a simulated user, and the secret task may be unknown to the automatic agent. At least one instruction to complete the secret task may be obtained from the simulated user according to at least one RL policy. At least one action may be generated by the automatic agent based on the at least one instruction and the at least one RL policy. Rewards may be determined for the simulated user and the automatic agent in response to determining that the at least one action successfully completes the secret task. The at least one RL policy may be adjusted based on the determined rewards.