Neural Network Training via Optimal Transport for Expert Imitation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for training neural networks to imitate expert behavior require direct interaction with the expert and struggle to efficiently generate long-term trajectories based on demonstration data.
Innovation Solution
A computer-implemented method using optimal transport theory to map the distribution of neural network-generated trajectories to expert demonstration data, enabling efficient training of a neural network without expert interaction, and allowing for the generation of long-term trajectories by determining a reward function that minimizes a transport cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing methods for training neural networks to imitate expert behavior are used, then expert interaction is required, but this reduces training efficiency and increases time cost
Solution Approach 1:
The patent uses optimal transport to create a mapping between the distribution of expert demonstration trajectories and the distribution of agent-generated trajectories. By copying the statistical structure of expert behavior through distribution matching rather than direct interaction, the system achieves efficient imitation learning without requiring continuous expert involvement.
Solution Approach 2:
The patent introduces an optimal transport mapping as an intermediary between expert demonstrations and agent training. This mapping serves as a mediator that transfers the essential characteristics of expert behavior to the agent through distribution matching, eliminating the need for direct expert-agent interaction during training.
2Duration of action of moving object
If existing methods are used to generate long-term trajectories, then the neural network struggles with efficiency, but this limits the ability to handle long-term scenarios
Solution Approach 1:
The patent changes the fundamental parameter being optimized from step-by-step trajectory generation to distribution-level matching. By working with entire trajectory distributions rather than individual steps, the method can efficiently handle long-term trajectories while maintaining training efficiency, as the optimal transport mapping captures long-term behavioral patterns in a single optimization process.
3Reliability
If direct expert interaction is required for training, then training can be performed, but this increases device complexity and operational difficulty
Solution Approach 1:
The system uses self-service by having the agent learn from pre-recorded expert demonstrations through optimal transport mapping, without requiring expert interaction during training. The expert data serves itself as the training source, eliminating the need for complex expert-in-the-loop training infrastructure while maintaining reliable training capability.
Data Source
Figure 1~2
Figure 3~5
Figure 6~7
AI summary
A computer-implemented method of training a first neural network depending on demonstration data, particularly expert demonstrations, comprising a plurality of trajectories being characterized by state-action-pairs, wherein said first neural network is configured to map states to actions, said method comprising: sampling a first number of trajectories from said first neural network, sampling a second number of trajectories from said demonstration data, determining a reward function associated with an optimal transport mapping a distribution of said first number of trajectories to a distribution of said second number of trajectories which minimizes a transport cost associated with said optimal transport depending on a predetermined transport cost function, training said first neural network depending on said optimal cost function.