Imitation Learning Policy Model Trained on State Data
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Solution Overview
Problem
Conventional imitation learning systems rely on action data, which can be unavailable or irrelevant, leading to inefficiencies and poor performance, especially when the demonstrator and agent have different control systems or when state data contains distractor features.
Innovation Solution
The proposed method trains a policy model using state data only, employing effect models (demonstrator and imitator models) that generate probability distributions for state data without relying on action data, allowing for imitation learning even when action data is not available, and ignoring irrelevant information in the state data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional imitation learning systems use action data for training, then the system can learn from demonstrator behaviors, but the system fails when action data is unavailable or when the demonstrator and agent have different control systems
Solution Approach 1:
The patent extracts and removes action data from the imitation learning process, relying solely on state data (observations) to train the policy model. This extraction resolves the contradiction by making the system adaptable to different control systems while maintaining reliability through observation-only training, eliminating the need for action data compatibility between demonstrator and agent.
Solution Approach 2:
The patent creates a universal training approach where the policy model can be trained using only state data from any demonstrator, regardless of the demonstrator's control system. This multi-functionality allows the same training methodology to work across different domains and control systems, enhancing both adaptability and reliability simultaneously.
2Quantity of substance
If the system uses state data containing distractor features for training, then more data is available for learning, but the system performs poorly due to irrelevant information
Solution Approach 1:
The patent introduces a feedback mechanism through the discriminator that continuously evaluates the policy model's performance and provides guidance to refine its learning. This feedback loop enables the system to distinguish between relevant and irrelevant features in the state data, maintaining high precision even when training on abundant state data containing distractor features.
Solution Approach 2:
The patent changes the training parameters and objective functions to focus on the probability distribution of state data transitions rather than relying on action data. This parameter change allows the system to automatically filter out distractor features by learning from the statistical patterns in state transitions, achieving high precision despite the presence of irrelevant information in the abundant state data.
3Device complexity
If traditional imitation learning methods are used, then the system structure is simpler, but the system requires a discriminator and performs poorly with distractor features
Solution Approach 1:
The patent inverts the traditional imitation learning approach by removing the discriminator component and focusing solely on training the policy model to match the probability distribution of state data transitions. This inversion simplifies the system structure while improving reliability on distractor features, as the policy model learns directly from state transitions without being influenced by discriminator-based adversarial training.
Data Source
AI summary
A method is proposed of training a policy model to generate action data for controlling an agent to perform a task in an environment. The method comprises: obtaining, for each of a plurality of performances of the task, a corresponding demonstrator trajectory comprising a plurality of sets of state data characterizing the environment at each of a plurality of corresponding successive time steps during the performance of the task; using the demonstrator trajectories to generate a demonstrator model, the demonstrator model being operative to generate, for any said demonstrator trajectory, a value indicative of the probability of the demonstrator trajectory occurring; and jointly training an imitator model and a policy model. The joint training is performed by: generating a plurality of imitation trajectories, each imitation trajectory being generated by repeatedly receiving state data indicating a state of the environment, using the policy model to generate action data indicative of an action, and causing the action to be performed by the agent; training the imitator model using the imitation trajectories, the imitator model being operative to generate, for any said imitation trajectory, a value indicative of the probability of the imitation trajectory occurring; and training the policy model using a reward function which is a measure of the similarity of the demonstrator model and the imitator model.