Offline RL Learning Device Using Discrete Latent Variables
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Solution Overview
Problem
Offline reinforcement learning algorithms face challenges with approximation errors and extrapolation errors due to the dependence on the quality of the dataset, lacking online interactions with the target environment.
Innovation Solution
A learning device and method that includes a dataset acquisition unit, discrete latent variable estimation, optimal action learning, value function estimation, and identification of a discrete latent variable to maximize action value, using a mixed policy and variational lower bound optimization to improve learning performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If offline reinforcement learning is used to reduce interactions with the environment, then the number of interactions is reduced, but approximation errors and extrapolation errors increase due to dataset quality dependence
Solution Approach 1:
The patent introduces a discrete latent variable as an intermediary representation that captures essential characteristics of the dataset. This latent variable acts as a mediator between the dataset and the value function, enabling the model to generalize better and reduce approximation errors while still using offline data without online interactions.
Solution Approach 2:
The patent transforms the continuous state space into a discrete latent variable space through clustering or discretization. This parameter transformation allows the value function to operate on a simplified, structured representation, improving generalization and reducing extrapolation errors while maintaining the offline learning framework.
2Device complexity
If a simple policy is used to reduce complexity, then device complexity is reduced, but learning performance deteriorates due to inability to capture complex patterns
Solution Approach 1:
The patent segments the policy into multiple discrete policies corresponding to different latent variable clusters. Instead of using a single complex policy, the system divides the state space into multiple regions and applies simpler, specialized policies to each region, achieving both low complexity and high performance through division of labor.
Solution Approach 2:
The patent adds a discrete latent variable dimension to the policy structure, transforming a flat policy into a hierarchical structure. This additional dimension allows the policy to capture complex patterns by combining simple policies across different latent variable states, effectively increasing expressive power without increasing the complexity of individual policy components.
Data Source
AI summary
A learning device includes a dataset acquisition unit configured to acquire a dataset including state information and action information on which a policy is to be learned, a discrete latent variable estimation unit configured to estimate a discrete latent variable representing characteristics of features from the state information and the action information, an optimal action learning unit configured to learn an optimal action using the state information and the discrete latent variable, a value function estimation unit configured to learn an action value from the state information and the action information, and an identification unit configured to identify a discrete latent variable that maximizes the action value using a result from the optimal action learning unit and a result from the value function estimation unit.