Dynamic Reuse Prior Knowledge Reinforcement Learning
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Solution Overview
Problem
Reinforcement learning methods face slow learning speeds in complex domains, especially when initial performance is critical, and existing transfer learning methods have limitations due to assumptions about source and target agents' internal representations and demonstrations.
Innovation Solution
The Dynamic Reuse of Prior Knowledge (DRoP) system leverages third-party actor demonstrator data sets for pre-training neural networks, using online confidence-based performance analysis to dynamically integrate prior knowledge into the reinforcement learning agent's learning loop, allowing for adaptive selection of action sources and requesting additional data when confidence thresholds are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning is applied in complex domains, then the agent can learn to perform tasks autonomously, but the learning speed becomes slow and requires large number of epochs
Solution Approach 1:
The system performs preliminary action by pre-training the neural network on third-party actor demonstrator data sets before the main reinforcement learning task. This preliminary training provides initial knowledge and policies that accelerate subsequent learning, reducing the number of epochs needed and improving learning speed without sacrificing autonomy.
2Productivity
If transfer learning is used to leverage demonstrations, then learning speed can be boosted, but the method requires assumptions about source and target agent's internal representations
Solution Approach 1:
The system achieves universality by designing a transfer learning framework that works across different agent types and task domains without requiring specific assumptions about internal representations. The neural network architecture and training methodology are general-purpose, allowing the same approach to be applied to various reinforcement learning problems while maintaining adaptability.
3Productivity
If prior knowledge from multiple sources is integrated, then learning performance can be improved, but inconsistent prior knowledge can confuse the target agent
Solution Approach 1:
The system implements feedback mechanisms where the target agent evaluates the quality and consistency of prior knowledge from different sources during training. Inconsistent or low-quality demonstrations are identified and weighted less or discarded, while consistent high-quality knowledge is reinforced. This feedback loop ensures that only reliable prior knowledge influences the learning process, maintaining reliability while improving performance.
4Productivity
If the target agent relies heavily on prior knowledge, then initial performance can be improved, but the agent may struggle to balance prior knowledge with self-learned policy
Solution Approach 1:
The system applies dynamics by making the balance between prior knowledge and self-learned policy adaptive rather than static. The weighting between demonstrated policies and learned policies changes dynamically based on training progress, task context, and performance metrics. This dynamic balancing reduces complexity by allowing the system to automatically adjust rather than requiring manual tuning of multiple parameters.
Data Source
AI summary
Systems, methods, and computer readable media directed to interactive reinforcement learning with dynamic reuse of prior knowledge are described in various embodiments. The interactive reinforcement learning is adapted for providing computer implemented systems for dynamic action selection based on confidence levels associated with demonstrator data or portions thereof.