Reinforcement Learning Action Selection for Response Speed
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Solution Overview
Problem
Existing reinforcement learning methods face challenges in quickly responding to environmental changes due to long response times and inefficient action selection cycles, leading to difficulties in realizing operations corresponding to selected actions and slow learning.
Innovation Solution
A machine learning method that receives previous auxiliary variable values and current sensor data to select and execute actions, updating parameters based on rewards and next sensor data, with a mechanism to increase the selection of previous actions as current actions, allowing for efficient learning even when action selection cycles are shorter than response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the cycle for selecting and executing actions is shortened to respond quickly to environmental changes, then the response speed is improved, but the operation corresponding to the selected action cannot be realized due to insufficient execution time
Solution Approach 1:
The patent applies preliminary action by selecting and preparing actions in advance based on predicted future states. The system uses a policy network to select actions not just for the current state but anticipating future environmental changes, allowing the control target to execute operations continuously without waiting for repeated action selection cycles. This resolves the contradiction by preparing actions beforehand so they can be executed reliably even when the selection cycle is short.
Solution Approach 2:
The patent implements dynamics by adapting the action selection strategy based on the relationship between action selection cycle and response time. When the response time is longer than the action selection cycle, the system adjusts its policy to select actions that account for this temporal mismatch, dynamically modifying the control strategy to ensure proper execution timing while maintaining fast response capability.
2Speed
If the action selection cycle is shorter than the response time, then quick response to environmental changes is achieved, but the clarity of action-reward relationships deteriorates making learning difficult
Solution Approach 1:
The patent applies feedback by using a value network to evaluate the expected future rewards of selected actions and incorporating this information into the learning process. The system receives reward signals and uses them to update both the policy network and value network, creating a closed-loop feedback mechanism that maintains clear action-reward relationships even when actions are selected faster than the response time. This allows the system to learn effectively despite the shortened action selection cycle.
Solution Approach 2:
The patent uses preliminary action in conjunction with feedback by predicting future states and evaluating potential rewards before actions are executed. The value network estimates future rewards based on predicted trajectories, providing advance feedback about which actions will lead to better outcomes. This preliminary evaluation maintains clear action-reward relationships by establishing expected value associations before the actual action-execution-reward cycle completes.
3Adaptability or versatility
If actions are selected frequently to adapt to changing environments, then adaptability is improved, but the consistency of action execution deteriorates leading to incomplete operations
Solution Approach 1:
The patent resolves this contradiction by selecting actions in advance based on predicted future states and maintaining these selections across multiple time steps. Instead of re-selecting actions at every cycle, the system determines actions preliminarily and executes them consistently over the duration needed for complete operation, while still adapting to environmental changes through continuous state prediction and policy updates.
Solution Approach 2:
The patent implements dynamics by making the action selection strategy adaptive to the temporal relationship between selection cycles and response times. The system dynamically adjusts its behavior based on whether the response time is longer or shorter than the action selection cycle, modifying the policy to maintain both adaptability to environmental changes and consistency in action execution through appropriate temporal smoothing of action selections.
Data Source
AI summary
According to one embodiment, a learning method, comprises receiving a first signal including a previous auxiliary variable value, previous action information regarding a previous action, or a set of previous scores, receiving current sensor data, selecting a current action of the control target based on the first signal, the current sensor data, and a parameter for obtaining a score from sensor data, causing the control target to execute the current action, receiving next sensor data and a reward, and updating the parameter based on the current sensor data, current action information regarding the current action, the next sensor data, and the reward. A degree of selecting a previous action as the current action is increased.