Regional Reinforcement Learning Models for Environment Shifts
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Solution Overview
Problem
Reinforcement learning systems face challenges in adapting to environmental changes, leading to forgotten training results when the environment shifts, necessitating retraining when the agent returns to a previous environment.
Innovation Solution
A training device that updates models for specific regions within a state space based on samples, allowing for evaluation and adaptation of actions within those regions, using a model-setting and clustering mechanism to determine the appropriate region for a given sample, thereby enabling more effective handling of environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning is performed in a new environment, then the agent can adapt to the new environment, but the training results from the past environment are forgotten
Solution Approach 1:
The state space is divided into multiple regions, and separate models are maintained for each region. When the environment changes, the agent can switch between region-specific models rather than forgetting all past training, as each region's model preserves its training results independently
Solution Approach 2:
The state space is pre-divided into regions and models are prepared for each region before environmental changes occur. This preliminary segmentation allows the system to quickly adapt to new environments by selecting appropriate regional models without retraining from scratch
2Reliability
If the agent is retrained in the past environment after environmental changes, then the past training results can be recovered, but time is lost due to retraining
Solution Approach 1:
Multiple regional models are prepared in advance for different environmental conditions. When the environment changes, the system can immediately switch to the appropriate pre-trained regional model rather than undergoing time-consuming retraining
Solution Approach 2:
The system dynamically selects which regional model to use based on the current environmental state. This dynamic model selection allows the agent to adapt to environmental changes instantly without the time loss associated with retraining
Data Source
AI summary
A training device updates, among a plurality of models, a model for a region that includes a given sample based on the sample. The plurality of models are provided for each region obtained by dividing a state space that includes a sample indicates a state about a control object and the plurality of models represent an evaluation of an action of the control object in response to control over the control object. The training device evaluates an action of the control object in a given state, based on a model for a region that includes a sample indicating the state.