Transfer Training for Autonomous Robot Agents
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Solution Overview
Problem
Training autonomous robot control systems using deep reinforcement learning is time-consuming and resource-intensive, especially when environmental conditions change, requiring agents to be retrained from scratch for each new situation.
Innovation Solution
Implementing a transfer training method where a pre-trained agent in one environmental condition is used to quickly adapt and train a new agent in a different condition through deep reinforcement training, leveraging existing knowledge to reduce training time and resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep reinforcement training is performed from scratch for each environmental condition, then the agent can be optimized for that specific field, but the training time and computing resources increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training agents in source environmental conditions before they are needed for target conditions. The pre-trained agents serve as a foundation that can be quickly adapted to new environments, avoiding the need to train from scratch and significantly reducing training time while maintaining optimization for specific fields
Solution Approach 2:
The patent uses pre-trained agents as intermediaries between source environmental conditions and target environmental conditions. These pre-trained agents transfer knowledge and skills to new conditions through adaptation, acting as a bridge that enables rapid deployment without complete retraining
2Reliability
If deep reinforcement training is performed from scratch for each environmental condition, then the agent can be optimized for that specific field, but the computing resources and cost increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training agents in source environmental conditions before they are needed for target conditions. The pre-trained agents serve as a foundation that can be quickly adapted to new environments, avoiding the need to train from scratch and significantly reducing training time while maintaining optimization for specific fields
Solution Approach 2:
The patent applies discarding and recovering by retaining the valuable knowledge and skills learned by pre-trained agents while discarding the computational resources needed for complete retraining. The pre-trained agents' learned policies and representations are recovered and reused in target conditions, significantly reducing computing resource requirements
3Adaptability or versatility
If separate training is performed for each problem in the real environment, then the agent can be specifically optimized for that problem, but the training time and computing resources increase significantly
Solution Approach 1:
The patent applies universality by developing a multi-functional training approach where pre-trained agents can serve multiple environmental conditions and problem types. The pre-trained knowledge base serves as a universal foundation that can be adapted to various specific problems, enabling one training system to support multiple applications
Data Source
AI summary
An agent training method based on transfer training is provided. The method includes preparing an agent pre-trained in a first environmental condition (hereinafter referred to as source agent), obtaining training data for training of an agent to be trained in a second environmental condition (hereinafter referred to as target agent) different from the first environmental condition by using the source agent, pre-training the target agent based on the training data, and performing deep reinforcement training-based training on the pre-trained target agent in the second environmental condition.