Active Imitation Learning Policy for Expert-Guided Vehicle Control
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Solution Overview
Problem
Existing imitation learning methods either require a fully interactive expert demonstrator or lack any expert interaction, leading to inefficiencies and potential distribution shift issues, especially in complex tasks like autonomous driving and off-road vehicle control.
Innovation Solution
An algorithmic approach for active imitation learning that allows an agent to request demonstrations from an expert, collecting entire trajectories rather than fine-grained information, and iteratively refines the policy using uncertainty-based state selection to converge on expert behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fully interactive expert demonstrator is used for imitation learning, then the agent can obtain detailed guidance, but the complexity of expert interaction and data collection increases significantly
Solution Approach 1:
The patent extracts only the essential information needed for imitation learning by having the expert provide demonstrations at key decision points rather than continuous interaction. The expert's complex behavior is distilled into actionable trajectory data that captures the essence of expert performance without requiring ongoing expert involvement.
Solution Approach 2:
The patent performs preliminary data collection by gathering expert demonstrations before the actual learning process begins. The expert provides demonstrations in advance, creating a dataset that the agent can learn from independently, eliminating the need for continuous expert interaction during training and deployment.
2Ease of operation
If zero access to expert is used for imitation learning, then the system is simpler to operate, but distribution shift issues occur and learning effectiveness decreases
Solution Approach 1:
The patent introduces an intermediary expert demonstrator that bridges the gap between complete expert interaction and zero expert access. The expert provides preliminary demonstrations that serve as a mediator, allowing the agent to learn from expert behavior without requiring continuous expert involvement, thus maintaining reliability while improving ease of operation.
3Measurement precision
If fine-grained expert information is collected, then the learning data quality improves, but the data collection process becomes more complex and time-consuming
Solution Approach 1:
The patent extracts only the critical portions of expert behavior needed for effective learning. Instead of collecting continuous fine-grained data, the system captures demonstrations at key decision points and transitions, obtaining sufficient learning signal with minimal data collection time.
Solution Approach 2:
The patent collects slightly more data than the absolute minimum by gathering complete trajectory demonstrations rather than just critical moments. This partial excess ensures sufficient coverage of expert behavior patterns while still being more efficient than comprehensive fine-grained collection.
4Productivity
If the agent requests entire demonstrations from expert, then the data collection is more realistic and efficient, but the amount of data to process increases
Solution Approach 1:
The patent extracts essential trajectory information from complete expert demonstrations. By focusing on key states, actions, and transitions rather than processing every detail of the full demonstration, the system maintains data collection efficiency while reducing the processing burden of large data volumes.
Data Source
Figure 1~3
AI summary
The invention relates to a computer-implemented method for providing a trained policy (132) configured to control a device (100). The device (100) is configured to solve a task in a working environment. The method comprises a step of initialising a given policy (112) for solving the task and a dataset (114) representing the task to be solved and including initial states for solving the task. The method further comprises a step of executing an algorithm (125) for an active imitation learning agent using the given policy (112) and the dataset (114). The algorithm (125) comprises solving the task using the given policy (112) starting from each of the initial states, selecting initial states depending on a likelihood of the task being solved differently by the agent and by an expert both starting from the same initial state, and appending a demonstration dataset (122) for each selected initial state to the dataset (114) to produce an extended dataset (124). Each demonstration dataset (122) represents an expert policy for solving the task starting from the selected initial state. The method also comprises a step of training the given policy (112) on the extended dataset (124), in order to provide the trained policy (132).