Neural Instruction Sequence Generation for Non-Differentiable Simulators
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Solution Overview
Problem
Conventional generative systems require the generator component to be differentiable, limiting their applicability and efficiency, and struggle with data hunger and the difficulty of injecting prior knowledge.
Innovation Solution
A generative adversarial neural network system that includes a reinforcement learning neural network subsystem and a discriminator neural network, allowing control of non-differentiable simulators, such as CAD programs or robot simulators, through a sequence of actions, with a training mechanism that uses a reward signal from the discriminator to update the generator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional generative systems use differentiable generator components, then gradient backpropagation is possible for training, but applicability to non-differentiable simulators is limited
Solution Approach 1:
The patent introduces a discriminator neural network as an intermediary component that bridges the generator and the non-differentiable simulator. The discriminator receives outputs from both the generator and real data, and its output is used to compute a reward signal for reinforcement learning. This intermediary structure allows gradient flow through the discriminator while avoiding the need for gradient backpropagation through the non-differentiable simulator, thereby enabling applicability to non-differentiable simulators without requiring complex alternative training mechanisms.
2Measurement precision
If conventional generative systems use large amounts of training data, then model accuracy improves, but data hunger increases computational and storage costs
Solution Approach 1:
The patent implements a feedback mechanism where the discriminator evaluates generated outputs and provides a reward signal back to the generator through reinforcement learning. This feedback loop allows the system to iteratively improve output accuracy by learning from the difference between generated and real data distributions. The feedback mechanism enables the system to achieve high accuracy with fewer training data samples because the reward signal guides the generator to focus on improving specific aspects of output quality rather than requiring exhaustive data coverage.
3Adaptability or versatility
If conventional generative systems process complex tasks, then task capability improves, but computational resource consumption increases
Solution Approach 1:
The patent segments the generative system into two distinct components: a generator that produces outputs and a discriminator that evaluates outputs. This segmentation allows each component to be optimized for its specific function. The discriminator, being a neural network, can efficiently process complex patterns and provide nuanced feedback signals. The generator uses reinforcement learning with this feedback to learn complex tasks. This segmentation enables handling of complex tasks with reduced computational overhead compared to monolithic generative models that must handle both generation and evaluation functions.
4Productivity
If conventional generative systems are trained without prior knowledge injection, then training simplicity is maintained, but learning efficiency decreases
Solution Approach 1:
The patent incorporates prior knowledge injection mechanisms that prepare and integrate domain-specific knowledge before the main training process begins. This preliminary action allows the system to start training with pre-loaded information about the task domain, which guides the reinforcement learning process and accelerates convergence. The prior knowledge is integrated into the discriminator or generator architecture, enabling the system to leverage existing understanding rather than learning everything from scratch, thereby improving training efficiency without requiring overly complex training mechanisms.
Data Source
AI summary
Methods, systems, and apparatus for providing a sequence of actions to perform a task. In one aspect, a method comprises: using a policy neural network to, at each of a sequence of time steps, select one or more actions to be performed according to an action selection policy learned by the policy neural network; providing the selected one or more actions to a simulator; implementing the selected one or more actions for the time steps using the simulator to generate a simulator output; discriminating between the simulator output and training data using a discriminator neural network to produce a discriminator output; and updating parameters of the policy recurrent neural network using a reinforcement learning procedure according to a reward signal determined from the discriminator output; and updating parameters of the discriminator neural network according to a difference between the simulator output and the training data.