Reinforcement Learning Task Decomposition for Dynamic Environments
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Solution Overview
Problem
Reinforcement learning in real-world environments with unobservable dynamic changes leads to performance degradation due to deviation from Markov decision processes, making stable learning challenging even with recurrent neural networks.
Innovation Solution
A method and apparatus for reinforcement learning using structural learning-based task decomposition, which involves selecting paired transitions with time-invariant and time-variant characteristics using a cycle generative adversarial network, training an autoencoder to embed these transitions into latent spaces, and performing reinforcement learning with orthogonal gradient descent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning is performed in a real-world environment with unobservable dynamic changes, then the system can operate in realistic conditions, but learning performance significantly degrades due to deviation from Markov decision process assumptions
Solution Approach 1:
The patent segments the environment into multiple sub-environments based on unobservable state changes (e.g., wind conditions in drone flight). Each sub-environment is modeled separately with its own MDP, allowing the system to maintain reliable learning within each segment while adapting to the overall real-world variability. This segmentation resolves the contradiction by enabling real-world operation through multi-environment modeling while preserving learning reliability within each segmented environment.
Solution Approach 2:
The patent introduces an intermediary component (environmental state estimator or gateway) that infers unobservable environmental changes from observable data. This intermediary bridges the gap between the agent and the non-stationary environment, allowing the reinforcement learning system to operate in real-world conditions while maintaining reliable learning by compensating for unobservable changes through the intermediary's state estimation.
2Adaptability or versatility
If a recurrent neural network model is used to handle non-stationary environments, then the system can process sequential data, but stable learning remains impossible due to the complexity and quick changes of real-world environments
Solution Approach 1:
The patent segments the non-stationary environment into multiple stationary sub-environments, each satisfying MDP assumptions. By dividing the complex dynamic environment into smaller stable segments, the system achieves learning stability within each segment while maintaining adaptability to overall environmental changes through the multi-environment framework. This segmentation approach resolves the contradiction better than RNNs alone by providing structural stability.
Solution Approach 2:
The patent dynamically selects or switches between different environmental models based on inferred environmental states. Rather than using a fixed RNN architecture, the system adapts its modeling approach by switching between multiple MDP models corresponding to different environmental conditions, achieving both stability within each model and adaptability across changing conditions.
3Stability of the object's composition
If task decomposition is applied to separate time-invariant and time-variant characteristics, then learning stability improves, but the system complexity increases due to multiple paired transitions and cycle GAN
Solution Approach 1:
The patent segments transitions into paired transitions with distinct time-invariant and time-variant characteristics. This segmentation of the learning task allows stable learning of invariant patterns while separately handling variant changes, resolving the contradiction by organizing complexity into structured, manageable segments that improve stability.
Solution Approach 2:
The patent uses cycle GAN to generate synthetic paired transitions that copy the essential characteristics of real environmental changes. By creating artificial training data that replicates time-variant patterns, the system achieves learning stability without requiring exhaustive real-world data collection, thereby managing system complexity while maintaining stability.
Data Source
AI summary
According to an exemplary embodiment of the present invention, a reinforcement learning method using a task decomposition inference model in a time-variant environment includes selecting a plurality of paired transitions having a time-invariant common characteristic and a time-variant different environmental characteristic from a dataset including a plurality of transition data, based on a cycle generative adversarial network (GAN), training an auto encoder to embed each of the time-variant part and the time-invariant part with respect to the plurality of paired transitions into a latent space, and performing reinforcement learning on a transition corresponding to data collected in the time-variant environment, using the trained auto encoder.