Target-Oriented Reinforcement Learning for Fast Convergence
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Reinforcement learning methods face inefficiencies due to the need for extensive trial and error in learning optimal actions, especially in rare rewarding situations, leading to low learning efficiency and effectiveness.
Innovation Solution
The proposed target-oriented reinforcement learning method involves collecting target data during reinforcement learning, using this data for auxiliary learning, and incorporating the results into the reinforcement learning process to enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the agent performs countless actions through trial and error to learn optimal actions, then the agent can determine optimal actions through reinforcement learning, but the learning efficiency is low and the process is time-consuming
Solution Approach 1:
The patent applies preliminary action by performing offline target learning before reinforcement learning. The system pre-processes target data and learns target features in advance, so that during the actual reinforcement learning process, the agent can directly utilize this pre-learned target information without needing to perform countless trial and error actions to discover targets, significantly reducing the learning time while maintaining learning accuracy
Solution Approach 2:
The patent introduces an intermediary mechanism by adding a target learning module that acts as a bridge between raw environmental data and the reinforcement learning agent. This intermediary pre-processes and structures target information, making it easier for the agent to utilize during reinforcement learning, thereby reducing the trial and error process needed while maintaining accurate target identification
2Adaptability or versatility
If the agent performs exploration in states without target information, then the agent can learn optimal actions, but the effectiveness is low especially in rare rewarding situations
Solution Approach 1:
The patent applies preliminary action by pre-learning target features from offline data before the reinforcement learning process. This pre-processing creates a enriched state representation that includes target information, enabling the agent to perform more effective exploration even in rare rewarding situations, thereby improving learning effectiveness without sacrificing adaptability
Solution Approach 2:
The patent changes the parameter of state representation by incorporating pre-learned target features into the state space. This transformation enriches the state information available to the agent, allowing it to better distinguish between different situations and improve learning effectiveness, particularly in rare rewarding scenarios where target information is crucial
Data Source
AI summary
A target-oriented reinforcement learning method according to an embodiment includes: collecting data related to the target of reinforcement learning as target data in the process of performing the reinforcement learning; learning the collected target data as auxiliary learning for the reinforcement learning; and incorporating the results of the learning of the target data into the performance of the reinforcement learning.