Causal Environment Model for Reinforcement Learning State Prediction
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Solution Overview
Problem
Reinforcement learning systems often fail to make accurate predictions of state transitions due to incorrect causal reasoning, leading to suboptimal actions and potential risks, especially when simulating environments with high-dimensional observations.
Innovation Solution
The system employs an environment model that uses latent representations to simulate state transitions, providing additional information on frontdoor or backdoor variables in causal reasoning, allowing for causally correct simulations without requiring a full simulator, and using a partial view of the environment to reduce computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a full simulator of the environment is used for planning iterations, then accurate state transition predictions can be achieved, but computational resources and system complexity increase significantly
Solution Approach 1:
The patent extracts only the essential components needed for planning from the full environment simulator. Instead of using a complete simulator, it isolates and trains a separate environment model that specifically handles state transition predictions required for planning iterations, removing unnecessary simulation components to reduce complexity while maintaining prediction accuracy
Solution Approach 2:
The system segments the environment modeling task into two distinct components: a full simulator for comprehensive environment simulation and a specialized environment model for planning-specific state transition predictions. This segmentation allows each component to be optimized for its specific purpose, reducing the complexity burden on the planning system
2Loss of information
If high-dimensional observations are processed for environment simulation, then comprehensive state information is captured, but computational resources increase
Solution Approach 1:
The patent extracts and processes only the relevant features from high-dimensional observations that are necessary for accurate state transition predictions. The environment model is trained to identify and process critical state variables while filtering out redundant information, reducing computational resource requirements while maintaining prediction accuracy
Solution Approach 2:
The system applies different processing quality to different aspects of the environment model. High-dimensional observations are processed with full detail only where critical for causal relationships, while other aspects use reduced processing, optimizing the balance between information completeness and computational efficiency
3Device complexity
If conventional environment models are used without causal correctness, then computational simplicity is maintained, but prediction accuracy and reliability deteriorate
Solution Approach 1:
The patent changes the training parameters and objectives of the environment model to enforce causal correctness. By modifying the loss function and training procedure to specifically account for causal relationships between actions and state transitions, the model achieves higher prediction accuracy without requiring increased structural complexity
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using an environment model to simulate state transitions of an environment being interacted with by an agent that is controlled using a policy neural network. One of the methods includes initializing an internal representation of a state of the environment at a current time point; repeatedly performing the following operations: receiving an action to be performed by the agent; generating, based on the internal representation, a predicted latent representation that is a prediction of a latent representation that would have been generated by the policy neural network by processing an observation characterizing the state of the environment corresponding to the internal representation; and updating the internal representation to simulate a state transition caused by the agent performing the received action by processing the predicted latent representation and the received action using the environment model.