Partially Measurable System Control With GP-Based Policy Learning
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Solution Overview
Problem
Model-free reinforcement learning (MFRL) algorithms are data-inefficient, requiring a large number of interactions with the environment to solve control tasks, which is problematic for real-world applications, especially in mechanical systems where excessive trials can cause wear and tear.
Innovation Solution
The proposed solution is to adopt model-based reinforcement learning (MBRL) using Gaussian Processes (GPs) to build predictive models of the environment, allowing for more efficient data use and improved control action planning. Specifically, the Monte Carlo Probabilistic Inference for Learning Control (MC-PILCO) algorithm is introduced, which uses GPs for one-step-ahead system dynamics and particle-based methods for long-term state distribution approximation, enabling gradient-based policy optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If model-free reinforcement learning (MFRL) algorithms are used to solve control tasks, then the system can learn control policies without a predefined model, but a massive amount of interactions with the environment are required, leading to excessive wear and tear on mechanical systems
Solution Approach 1:
The patent applies preliminary action by building a predictive model of the environment dynamics before actual control execution. The system learns system dynamics models (equations 1-3) that predict future states based on current state and control inputs, allowing the controller to plan actions in advance using simulated trajectories rather than requiring massive real-world trial-and-error interactions
Solution Approach 2:
The patent creates a computational copy of the environment dynamics through learned system models. Instead of interacting directly with the physical system for all learning iterations, the controller uses the learned dynamic model to generate simulated trajectories and evaluate policies, significantly reducing the number of required real interactions with the mechanical system
2Device complexity
If deterministic models are used in model-based reinforcement learning, then the model structure is simple and computationally efficient, but model inaccuracy suffers dramatically, especially when uncertainty is not captured
Solution Approach 1:
The patent transforms the deterministic model into a stochastic model by adding noise terms to the state transitions (equation 3: x_{t+1} = f(x_t, u_t) + w_t). This parameter change allows the model to capture uncertainty in system dynamics while maintaining the same underlying functional structure, improving reliability without substantially increasing complexity
Solution Approach 2:
The patent introduces an intermediary stochastic element (noise term w_t) between the deterministic dynamics function f(x_t, u_t) and the actual state transition. This intermediary allows the model to represent uncertainty and variability in the environment while building upon the simpler deterministic framework, balancing complexity and accuracy
3Productivity
If moment matching is used to approximate state distribution in PILCO, then analytical computation of policy gradient is enabled, but only unimodal distributions can be modeled, limiting handling of multimodal initial conditions
Solution Approach 1:
The patent segments the state distribution representation by using multiple particles instead of a single Gaussian distribution. Each particle represents a possible state trajectory, allowing the system to represent multimodal distributions naturally. This segmentation enables handling of multiple initial conditions and uncertainty modes simultaneously
Solution Approach 2:
The patent transitions from representing the distribution in parameter space (mean and covariance of single Gaussian) to representing it in state space through multiple particle samples. This dimensional change from 2 parameters to N particles allows representation of complex multimodal distributions while maintaining computational tractability through Monte Carlo methods
4Productivity
If Squared Exponential (SE) kernels are used in Gaussian Processes, then computation of moments is tractable, but the kernel choice is very stringent and may show poor generalization properties in unseen data
Solution Approach 1:
The patent makes the kernel selection dynamic and flexible rather than fixed to SE kernels. The system can adaptively choose from different kernel types (RBF, linear, polynomial, Matern) based on the specific characteristics of the environment being modeled, allowing optimal generalization for different systems while maintaining computational efficiency through the particle-based approach that doesn't rely on analytical moment computation
Data Source
AI summary
A controller for controlling a system that includes a policy to control the system is provided. The controller includes an interface connected to the system, the interface acquires an action state and a measurement state via sensors measuring the system, a memory to store computer-executable program modules including a model learning module and a policy learning module, a processor that performs steps of the program modules. The steps include offline-modeling to generate offline-learning states based on the action state and measurement state using the model learning program, providing the offline states to the policy learning program to generate policy parameters, and updating the policy of the system to operate the system based on the policy parameters.