N-Step Return Regularization to Reduce Offline RL Distribution Shift
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Offline reinforcement learning methods face the distributional shift problem due to mismatches between learning and test datasets, leading to incorrect action value estimation.
Innovation Solution
An n-step return-based implicit regularization method and apparatus that includes a state value model, state-action value model, and policy model, using n-step returns to update parameters and exclude actions not in the dataset, reducing estimation errors through asymmetric loss functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If offline reinforcement learning is used to avoid online interaction losses, then economic and social losses are reduced, but distributional shift problem occurs due to dataset mismatch
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing return values for multiple time steps (n-step returns) during the offline learning phase. This allows the system to prepare comprehensive action value estimates in advance from the dataset, enabling accurate evaluations without requiring online interaction or risking distributional shift during deployment.
2Measurement precision
If n-step return-based implicit regularization is applied to address distributional shift, then action value estimation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the return calculation into discrete n-step return components, where each return value is computed for a specific time horizon n. This segmentation allows the system to process and store return values in a structured manner, improving estimation accuracy while managing computational complexity through systematic breakdown of the calculation process.
Solution Approach 2:
The patent utilizes parameter changes by introducing the discount factor and time step parameter n as variables in the return calculation. By adjusting these parameters, the system can adapt the balance between computational complexity and estimation accuracy, allowing flexible control over the trade-off without requiring complex architectural changes.
3Stability of the object's composition
If asymmetric loss function is used to reduce estimation error, then learning stability is improved, but optimization difficulty increases
Solution Approach 1:
The patent applies asymmetry by using an asymmetric loss function that treats positive and negative estimation errors differently. This asymmetric treatment prioritizes reducing overestimation errors in action values, thereby improving learning stability and ensuring that the policy learns from correct actions more effectively, while the increased optimization difficulty is managed through the structured nature of the n-step return calculations.
Data Source
AI summary
An offline reinforcement learning apparatus for n-step return-based implicit regularization is disclosed. The offline reinforcement learning apparatus comprises a processor; and a memory connected to the processor, wherein the memory comprises program instructions, in response to being executed by the processor, perform operations comprising, sampling, among datasets collected in a preset domain, some datasets including state, action, state at the next time point, reward, and return in n-step, calculating an objective function of a state value model that evaluates a value of a specific state using the sampled data set to update a parameter of the state value model, setting a TD (temporal difference) target based on the state value model, calculating an objective function of a state-action value model that evaluates a value of a specific state and action.