Inverse Constraint Learning with Transferable Reward Decomposition
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Solution Overview
Problem
Conventional Inverse Constraint Learning methods are limited to learning constraints requiring precise task reward information, lacking flexibility to adapt to new environments and lacking transferability of learned constraints.
Innovation Solution
An Inverse Constraint Learning technique that estimates a reward function using task-reward candidates, decomposes it into transferable and constraint reward functions, and trains a neural network in a new environment using the constraint reward function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If precise task reward information is used to learn constraints, then constraint learning accuracy is improved, but adaptability to new environments deteriorates
Solution Approach 1:
The patent introduces task-reward candidates as an intermediary between the demonstrations and the constraint learning process. Instead of directly using precise task reward information, the system uses multiple candidate reward functions to estimate the reward function that satisfies constraints, enabling both accurate constraint learning and adaptability to new environments
Solution Approach 2:
The patent changes the parameter representation from a single precise task reward value to multiple task-reward candidates. This parameter transformation allows the system to explore different reward interpretations and learn constraints that are both accurate and transferable to new environments through inverse reinforcement learning
2Adaptability or versatility
If task-reward candidates are used to estimate reward function, then transferability of constraints is improved, but information precision is worsened
Solution Approach 1:
The patent employs feedback through the inverse reinforcement learning process, where the system iteratively refines its estimate of the reward function by comparing predicted behaviors with actual demonstrations. This feedback mechanism recovers precise constraint information from imprecise task-reward candidates, maintaining both transferability and information accuracy
3Adaptability or versatility
If inverse reinforcement learning is performed with task-reward candidates, then constraint transferability is improved, but computational complexity increases
Solution Approach 1:
The patent segments the complex task-reward candidates into manageable components that can be processed through inverse reinforcement learning. By decomposing the reward estimation into discrete candidate functions and their corresponding constraints, the system achieves transferable constraints while managing computational complexity through structured processing
Data Source
AI summary
An Inverse Constraint Learning method for an electronic device according to one aspect comprises acquiring demonstrations and task-reward candidates in a first learning environment of a neural network by the electronic device; estimating a total reward function that satisfies constraints, based on the demonstrations and the task-reward candidates; decomposing the total reward function into a transferable task reward function and a constraint reward function; and training the neural network to perform learning in a second learning environment, based on the constraint reward function.