Skill Sequencing for Sparse-Reward Reinforcement Learning
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Solution Overview
Problem
Existing reinforcement learning approaches face challenges such as catastrophic forgetting and sub-optimal performance when reusing previously learned skills, particularly in sparse reward tasks, leading to inefficiencies and potential wear on agents.
Innovation Solution
A system that uses previously learned skills to explore environments for training data collection, freezing these skills to train an action selection system, allowing for flexible adaptation and combination of skills without constraining the learned solution, and facilitating exploration over extended time scales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If previously learned skills are reused through fine-tuning, then training efficiency is improved, but catastrophic forgetting occurs and useful information is lost
Solution Approach 1:
The system segments the action selection process into multiple independent skill action selection subsystems, each trained to perform a specific skill task. Instead of fine-tuning a single model which causes catastrophic forgetting, the system divides the knowledge into separate modules that can be independently selected and combined, preventing information loss while maintaining training efficiency.
Solution Approach 2:
The system changes the parameters of the skill execution by using a scheduler neural network that dynamically selects which skill subsystem to execute based on the current state. This allows the system to adaptively change which learned skills are applied without modifying the underlying skill models, thus avoiding catastrophic forgetting while maintaining flexibility.
2Productivity
If previously learned skills are used for exploration, then training data collection is improved, but the agent suffers wear and tear in real-world environments
Solution Approach 1:
The system performs preliminary exploration and data collection using previously learned skills before deploying the agent in the real-world environment. The skill subsystems are used to gather training data in advance, allowing the action selection system to be thoroughly trained and optimized before actual deployment, thereby reducing the wear and tear on the physical agent during operation.
3Adaptability or versatility
If a scheduler neural network is used to select skills, then flexible adaptation is improved, but system complexity increases
Solution Approach 1:
The scheduler neural network serves multiple functions: it selects which skill subsystem to execute, determines the duration of skill execution, and adapts to different task requirements. This multi-functional component provides flexible adaptation without requiring separate mechanisms for each function, thereby managing system complexity while maintaining versatility.
4Reliability
If reinforcement learning is used to learn tasks from scratch, then performance can be optimized, but training time is excessive and computational resources are consumed
Solution Approach 1:
The system performs preliminary training of multiple skill subsystems on specific skill tasks before combining them for the target task. This preliminary action allows each skill subsystem to be pre-trained and optimized, reducing the overall training time required when learning the complete task, while still enabling performance optimization through subsequent reinforcement learning on the action selection system.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling an agent that is interacting with an environment. Implementations of the system use previously learned skills to explore states of the environment to collect and store training data, which is then used to train an action selection system. The system includes a set of skill action selection subsystems, each configured to select actions for the agent to perform for a respective skill. The set of skill action selection subsystems is used to explore states of the environment to collect the training data, keeping their individual action selection policies unchanged. A scheduler neural network selects the skill neural networks to use. The action selection system is trained on the stored training data.