Dynamics-Aware Reward Function Comparison for Robot Control
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Solution Overview
Problem
Conventional methods for comparing reward functions in autonomous robotics systems, particularly learned reward functions, often produce unsatisfactory results due to out-of-distribution evaluations, leading to poor performance in real-world applications.
Innovation Solution
A dynamics-aware reward function comparison system that converts reward functions to a canonical form incorporating a transition model of the environment, allowing for accurate comparison by computing pseudometrics based on physically realizable state transitions, and selects a final reward function closer to a reference using these metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional reward function comparison methods are used, then the comparison process is simple, but the evaluation accuracy deteriorates due to out-of-distribution evaluations
Solution Approach 1:
The system performs preliminary actions by generating a transition model of the environment before comparing reward functions. This transition model captures the dynamics of the environment and is used to evaluate reward functions on physically realizable state transitions, preventing out-of-distribution evaluations. The reference reward function is also transformed using the transition model before comparison, ensuring both functions are evaluated under the same realistic conditions.
Solution Approach 2:
The transition model serves as an intermediary between the reward functions and the comparison process. Instead of directly comparing reward functions on arbitrary state transitions, the system uses the transition model to mediate the evaluation, ensuring that comparisons are based on physically realizable transitions. This intermediary structure enables accurate comparison while maintaining system manageability.
2Reliability
If reward functions are compared without considering environmental dynamics, then the comparison is computationally efficient, but the reliability deteriorates due to unrealistic evaluations
Solution Approach 1:
The system performs preliminary action by pre-computing the transition model that captures environmental dynamics before the reward function comparison. This transition model is then reused to evaluate multiple reward functions, ensuring reliability without repeating expensive dynamic computations. The reference reward function is transformed using this pre-computed model, ensuring consistent and reliable evaluation across all comparisons.
Solution Approach 2:
The system changes the evaluation parameters by shifting from arbitrary state transitions to transitions constrained by the transition model. This parameter change ensures that reward functions are evaluated on physically realizable state transitions, improving reliability. The pseudometric computation is performed on these constrained transitions, balancing reliability with computational efficiency.
3Measurement precision
If learned reward functions are evaluated on arbitrary state transitions, then the evaluation coverage is comprehensive, but the measurement precision deteriorates due to out-of-distribution samples
Solution Approach 1:
The system applies local quality by focusing the evaluation on locally relevant state transitions that are physically realizable according to the transition model. Instead of uniformly evaluating across all possible state transitions, the system concentrates evaluation on transitions that actually occur in the environment, improving measurement precision for learned reward functions while maintaining adaptability to the specific environment dynamics.
Data Source
AI summary
Systems and methods described herein relate to dynamics-aware comparison of reward functions. One embodiment generates a reference reward function; computes a dynamics-aware transformation of the reference reward function based on a transition model of an environment of a robot; computes a dynamics-aware transformation of a first candidate reward function based on the transition model; computes a dynamics-aware transformation of a second candidate reward function based on the transition model; selects, as a final reward function, the first or second candidate reward function based on which is closer to the reference reward function as measured by pseudometrics computed between their respective dynamics-aware transformations and the dynamics-aware transformation of the reference reward function; and optimizes the final reward function to control, at least in part, operation of the robot.