Multi-Step Inverse Models for Minimal Controllable Latent States
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Solution Overview
Problem
Existing techniques lack a general theoretical guarantee for discovering a minimal controllable latent state, which is essential for efficient control of agents in complex environments.
Innovation Solution
The Agent-Controllable State Discovery algorithm (AC-State) uses a multi-step inverse model with an information bottleneck to learn a minimal controllable latent state, ensuring that only relevant information for control is captured.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If one-step inverse models are used, then the model is simple and fast, but it fails to capture a complete controllable latent state
Solution Approach 1:
The patent segments the latent state capture process into multiple temporal steps. Instead of capturing the latent state in a single step, the model processes observations sequentially through multiple time steps, allowing it to accumulate and refine latent state information progressively. This segmentation enables the model to capture complete controllable latent states while maintaining computational efficiency.
Solution Approach 2:
The patent employs preliminary encoding of observations into latent representations before the main prediction task. The encoder processes observations in advance to create compressed latent states that capture essential information. This preliminary action allows the model to work with refined representations during the multi-step prediction process, improving both accuracy and efficiency.
2Loss of information
If models predict the entire observation space, then complete information is captured, but information unrelated to control is also captured increasing complexity
Solution Approach 1:
The patent extracts only the controllable components from the full observation space. The encoder is designed to selectively encode information that is relevant to control, filtering out extraneous details. This extraction process produces a compact latent state representation that contains precisely the information needed for control decisions, eliminating unnecessary complexity.
Solution Approach 2:
The patent applies different processing qualities to different parts of the observation space. Rather than uniformly processing all observations, the model focuses computational resources on capturing controllable latent variables while using simpler processing for irrelevant aspects. This local differentiation optimizes the balance between information capture and computational complexity.
3Reliability
If more information is captured in the latent state, then control accuracy improves, but the latent state size increases reducing efficiency
Solution Approach 1:
The patent uses dynamic latent state representations that adapt their content based on the task and environment. The latent state evolves over time through the multi-step process, selectively maintaining information that becomes relevant for control. This dynamic approach ensures the latent state contains precisely the right amount of information at each step, optimizing both accuracy and efficiency.
Solution Approach 2:
The patent changes the parameters of the latent state representation during the multi-step process. The encoder dynamically adjusts what information is encoded based on the current observation and temporal context. This parameter adaptation allows the model to capture sufficient information for accurate control while keeping the latent state compact for efficient processing.
Data Source
AI summary
Generally discussed herein are devices, systems, and methods for determining a minimal controllable latent state and operating a model trained to implement the minimal controllable latent state. A method can include receiving first and second observations of a temporal series of observations produced by a sensor, the second observation separated from the first observation by more than one other observation, the first observation associated with a first action performed by an agent responsive to the first observation, encoding, by an encoder, the first and second observations into respective first and second hidden state representations, combining the first and second hidden state representations resulting in a combined representation, operating, by an action predictor, on the combined representation resulting in a second action, and altering weights of the encoder and the action predictor based on a difference between the first and second actions.