Deep Reinforcement Learning Control for Stable Complex Tasks
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Solution Overview
Problem
Current control systems using reinforcement learning are limited by the need for hand-engineered policy classes and are unsuitable for learning complex tasks, as they often result in unstable gradients and are not effective for rich motion repertoires required in natural environments.
Innovation Solution
The implementation of deep reinforcement learning using convolutional neural networks or deep autoencoders to determine actions based on environmental observations, with guided policy search and deep Q-networks, allowing for the update of policy parameters and application in simulation environments to develop control policies for physical environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If hand-engineered policy classes are used in reinforcement learning, then the system is easier to implement, but the system cannot learn rich motion repertoires for complex tasks
Solution Approach 1:
The patent replaces hand-engineered policy classes (mechanical/design-based approach) with deep neural networks (data-driven approach). The DRL system learns motion policies automatically from observations and rewards without requiring manual policy design, thereby achieving rich motion repertoires while maintaining implementation feasibility through standardized deep learning frameworks.
2Loss of information
If direct optimization approach with neural network control is used, then the gradient of the policy can be propagated backwards in time, but the method produces highly unstable gradients
Solution Approach 1:
The patent introduces an intermediary representation layer between the neural network policy and the gradient computation. By using deep neural networks with appropriate architecture design and training techniques, the system enables stable gradient propagation through time while maintaining the ability to learn complex policies, effectively mediating between the need for gradient flow and gradient stability.
3Use of energy by moving object
If small controllers are used for simple tasks, then the system is computationally efficient, but the system cannot handle complex tasks that benefit from increased representational power
Solution Approach 1:
The patent employs dynamic controller architecture where the complexity of the neural network can be adapted to the task requirements. The system uses deep neural networks with learnable parameters that can scale representational power according to task complexity, allowing efficient computation for simple tasks while providing sufficient capacity for complex tasks through appropriate network architecture selection.
Data Source
AI summary
Data indicative of a plurality of observations of an environment are received at a control system. Machine learning using deep reinforcement learning is applied to determine an action based on the observations. The deep reinforcement learning applies a convolutional neural network or a deep auto encoder to the observations and applies a training set to locate one or more regions having a higher reward. The action is applied to the environment. A reward token indicative of alignment between the action and a desired result is received. A policy parameter of the control system is updated based on the reward token. The updated policy parameter is applied to determine a subsequent action responsive to a subsequent observation.