Conditional Episode Configuration for Non-Continuous State Learning
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Solution Overview
Problem
Reinforcement learning devices face limitations in applying to non-continuous states and environments, particularly in business scenarios where defining episodes is challenging, leading to inefficient training and potential overfitting.
Innovation Solution
A reinforcement learning device using a conditional episode configuration that imparts conditions on decision-making, terminating episodes when conditions are not met, maximizing the total sum of rewards by defining episodes through sampling and configuring conditions for states, actions, and rewards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning is applied to non-continuous states in business scenarios, then the applicability and versatility of reinforcement learning is improved, but the difficulty of defining episodes and configuring rewards increases
Solution Approach 1:
The patent segments the continuous state space into discrete episodes by introducing episode start and end conditions. This segmentation allows reinforcement learning to handle non-continuous states by breaking down the problem into manageable episodic segments, where each episode represents a complete decision-making cycle from start condition to end condition.
Solution Approach 2:
The patent introduces dynamic episode configuration where episode boundaries are not fixed but determined by configurable start and end conditions. This dynamic approach allows the system to adapt episode definitions based on the specific business scenario, making the reinforcement learning framework flexible enough to handle various non-continuous state problems.
2Ease of manufacture
If training is performed with fixed episode definitions, then the training process is simpler, but the training time increases exponentially with more episodes
Solution Approach 1:
The patent changes the parameters of episode configuration by introducing configurable start and end conditions instead of fixed episode definitions. This parameter change allows the system to optimize training efficiency by adjusting episode boundaries based on the problem characteristics, reducing unnecessary training iterations while maintaining simplicity in the training process.
3Productivity
If episodes are maintained in batches without considering state-reward relationships, then the training process is more efficient, but the agent may take incorrect actions due to overfitting
Solution Approach 1:
The patent introduces feedback mechanisms by configuring episode end conditions based on state-reward relationships. The agent receives feedback about whether an episode should terminate based on the current state and reward, which helps prevent overfitting by ensuring that training episodes reflect meaningful decision-making cycles rather than arbitrary batch boundaries.
Data Source
AI summary
Disclosed are a reinforcement learning device and method using a conditional episode configuration. The present invention imparts conditions on individual decision making, and terminates an episode if the imparted conditions are not met, thereby maximizing the total sum of rewards reflecting the current values. Accordingly, reinforcement learning can be easily applied even to problems using a non-continuous state.