Adversarial RL Control Sequences 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 difficulty in incorporating 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 the applicability to non-differentiable simulators is limited
Solution Approach 1:
The patent introduces a discriminator neural network as an intermediary between the generator and the loss function. The discriminator receives outputs from both the generator and real data, producing discrimination scores that are then used to compute the loss. This intermediary structure allows gradient flow through the discriminator while enabling the generator to control non-differentiable simulators, thus resolving the contradiction between adaptability and architectural constraints
Solution Approach 2:
The patent replaces the traditional direct gradient backpropagation mechanism (mechanical system) with a discriminator-based adversarial training mechanism. Instead of directly computing gradients through the generator and simulator, the system uses the discriminator's discrimination scores to guide generator updates, substituting the direct mechanical gradient flow with an adversarial learning process that works with non-differentiable components
2Measurement precision
If conventional generative systems process large amounts of data, then model accuracy improves, but computational resource consumption increases
Solution Approach 1:
The patent implements continuous adversarial training where the discriminator and generator engage in ongoing competitive learning. The discriminator continuously refines its discrimination capability while the generator continuously improves its output quality, creating a continuous useful action loop that achieves high accuracy without requiring exhaustive data processing at each stage, thus improving efficiency
Solution Approach 2:
The generative system performs self-service through the adversarial training mechanism where the discriminator automatically identifies patterns and deficiencies in generated data, providing self-correction signals to the generator. This self-service capability allows the system to improve model accuracy through iterative self-refinement rather than requiring external manual intervention or processing of increasingly large datasets
3Measurement precision
If conventional generative systems lack prior knowledge incorporation, then system simplicity is maintained, but performance on complex tasks deteriorates
Solution Approach 1:
The patent incorporates prior knowledge through preliminary action by pre-training the discriminator on real data distributions before adversarial training begins. The discriminator learns authentic patterns and characteristics of the target domain in advance, creating a knowledgeable baseline that guides the generator's learning process. This preliminary knowledge acquisition enables the system to perform complex tasks more effectively without substantially increasing structural complexity
Data Source
AI summary
A generative adversarial neural network system to provide a sequence of actions for performing a task. The system comprises a reinforcement learning neural network subsystem coupled to a simulator and a discriminator neural network. The reinforcement learning neural network subsystem includes a policy recurrent 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, each action comprising one or more control commands for a simulator. The simulator is configured to implement the control commands for the time steps to generate a simulator output. The discriminator neural network is configured to discriminate between the simulator output and training data, to provide a reward signal for the reinforcement learning. The simulator may be non-differentiable simulator, for example a computer program to produce an image or audio waveform or a program to control a robot or vehicle.