Learning Device Using Neural ODE Gradient Model for Control Policy
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Solution Overview
Problem
Current reinforcement learning methods for control targets are inefficient, requiring extensive time to learn optimal control policies and reward functions, especially when lacking prior learning results.
Innovation Solution
A learning device and method that utilizes a neural ordinary differential equation as a gradient model to learn the relationship between a control target's state, control actions, and temporal changes, enabling faster learning by leveraging previous task results for initial policy and reward function determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If reinforcement learning is performed without prior learning results, then control over the control target can be learned, but the learning time becomes excessively long
Solution Approach 1:
The patent applies preliminary action by learning a gradient model beforehand that captures the temporal changes of the control target's state. This pre-learned model serves as a foundation for subsequent control learning, eliminating the need to start from scratch and significantly reducing the time required to learn optimal control policies.
Solution Approach 2:
The gradient model acts as an intermediary between the control target's state and the control policy. By introducing this intermediate representation that encodes temporal dynamics, the system can leverage it to accelerate the reinforcement learning process and achieve faster convergence to optimal control strategies.
2Measurement precision
If extensive reinforcement learning is performed to achieve accurate control policies, then control accuracy improves, but the time required for learning increases significantly
Solution Approach 1:
The system performs preliminary learning of the gradient model to capture temporal changes in the control target's state. This pre-computed model provides accurate representations of system dynamics that can be directly utilized during control policy learning, achieving high control accuracy without requiring extensive learning time.
Solution Approach 2:
The patent changes the approach by introducing a gradient model parameterization that represents temporal changes. This parameter change enables the system to encode complex temporal dynamics in a compact form, allowing for accurate control policies to be learned more efficiently by leveraging the pre-learned gradient model parameters.
Data Source
AI summary
A learning device includes at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: perform reinforcement learning of control over a control target; use data used in the reinforcement learning to learn a model that shows the relationship between a state relating to the control target, control over the control target, and a temporal change in the state relating to the control target; and use the model and the result of the reinforcement learning to learn control over the control target.