Adversarial Reinforcement Learning for Diverse Failure Scenarios
Solution Overview
Problem
Adversarial reinforcement learning tends to create repetitive and trivial failure scenarios for decision-making algorithms, such as autonomous driving systems, which limits their effectiveness in improving algorithm robustness and safety.
Innovation Solution
A method involving the storage and simulation of diverse failure experiences for a player agent, where a similarity threshold is used to determine the addition of new experiences to a storage, and adversarial agents are trained using a combination of failure and success experiences to generate diverse adversarial scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adversarial reinforcement learning is used to generate failure scenarios, then the decision-making algorithm can be trained to handle failure cases, but the generated scenarios become repetitive and trivial
Solution Approach 1:
The patent inverts the traditional reinforcement learning approach by training an adversarial agent to deliberately cause failures rather than achieve success. The adversarial agent maximizes a loss function designed to make the player agent fail, thereby generating diverse failure scenarios for robustness training
Solution Approach 2:
The patent changes the objective parameter from maximizing reward (traditional RL) to minimizing reward (adversarial RL). By inverting the reward signal and training the adversarial agent to minimize the player agent's cumulative reward, the system generates varied failure modes instead of repetitive successful patterns
2Reliability
If adversarial agents are trained to maximize failure impact, then failure scenarios are generated, but the agents focus on trivial and repetitive failure modes
Solution Approach 1:
The patent converts the harmful effect of adversarial attacks (causing failures) into a beneficial training mechanism. By deliberately exposing the decision-making algorithm to diverse failure scenarios generated by the adversarial agent, the system improves robustness against real-world failures while transforming the adversarial harm into a protective benefit
Solution Approach 2:
The patent implements a feedback loop where the adversarial agent's actions are evaluated based on the player agent's failure, and the training updates the adversarial policy accordingly. The loss function provides continuous feedback to guide the adversarial agent toward generating more effective and diverse failure scenarios
Data Source
AI summary
Making failure scenarios using adversarial reinforcement learning is performed by storing, in a first storage, a variety of first experiences of failures of a player agent due to an adversarial agent, and performing a simulation of an environment including the player agent and the adversarial agent. It also includes calculating a similarity of a second experience of a failure of the player agent in the simulation and each of the variety of first experiences in the first storage, and updating the first storage by adding the second experience as a new first experience of the variety of first experiences in response to the similarity being less than a threshold. Additionally, the use of adversarial reinforcement learning can include training the adversarial agent by using at least one of the plurality of first experiences in the first storage to generate an adversarial agent having diverse experiences.