Robust Reinforcement Learning Handles Temporally-Coupled Perturbations Using Game-Theoretic Approach

Core Concepts

A novel game-theoretic approach, GRAD, that treats the temporally-coupled robust RL problem as a partially-observable two-player zero-sum game, optimizing for general robustness against temporally-coupled perturbations.

Abstract

The paper introduces a novel class of temporally-coupled adversarial attacks to identify the limitations of prior threat models as a challenge for existing robust RL methods. It then proposes a game-theoretic response approach, GRAD, that models the interaction between the agent and the temporally-coupled adversary as a two-player zero-sum game. GRAD employs Policy Space Response Oracles (PSRO) to find an approximate equilibrium, enabling the agent to adapt to the adversary's strategies and achieve robust performance.
The key highlights are:

Formal definition of temporally-coupled perturbations, which constrain the perturbations at each timestep based on the previous timestep's perturbation. This represents a more realistic challenge compared to the standard non-temporally-coupled perturbations.
Introduction of GRAD, a game-theoretic approach that treats the robust RL problem as a partially-observable two-player zero-sum game. GRAD leverages PSRO to find an approximate equilibrium, allowing the agent to adapt to the adversary's strategies.
Empirical evaluation on continuous control tasks, demonstrating GRAD's superior robustness against both non-temporally-coupled and temporally-coupled adversaries across diverse attack domains, including state perturbations, action perturbations, and mixed perturbations.

Stats

The paper does not provide specific numerical data or statistics to support the key claims. Instead, it presents the results in the form of average episode rewards under different attack settings, comparing the performance of GRAD and baseline methods.

Quotes

"Deploying reinforcement learning (RL) systems requires robustness to uncertainty and model misspecification, yet prior robust RL methods typically only study noise introduced independently across time."
"However, the set of perturbations faced in the real world are typically temporally-coupled: if the wind blows in one direction at one time step, it will likely blow in a similar directly at the next step."
"GRAD is more general than prior adversarial defenses in the sense that it does not target certain adversarial scenarios and converges to the approximate equilibrum training with an adversary policy set."

Key Insights Distilled From

Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations

by Yongyuan Lia... at arxiv.org 04-10-2024

https://arxiv.org/pdf/2307.12062.pdf

Deeper Inquiries

How can the game-theoretic framework of GRAD be extended to handle more complex real-world environments, such as partially observable or multi-agent settings

The game-theoretic framework of GRAD can be extended to handle more complex real-world environments by incorporating techniques for dealing with partially observable or multi-agent settings. In the case of partially observable environments, the framework can integrate methods like Partially Observable Markov Decision Processes (POMDPs) to account for the agent's limited knowledge of the environment. This can involve using belief states to represent the agent's uncertainty about the environment state and updating these beliefs based on observations. Additionally, techniques like Recurrent Neural Networks (RNNs) can be employed to capture temporal dependencies in partially observable settings.
For multi-agent settings, the GRAD framework can be adapted to consider interactions between multiple agents by modeling the environment as a multi-agent system. This would involve defining the strategies and objectives of each agent, incorporating the actions and observations of other agents into the decision-making process, and potentially using techniques like multi-agent reinforcement learning to learn optimal policies in a competitive or cooperative setting. By extending GRAD to handle these complexities, the framework can be applied to a wider range of real-world scenarios with more realistic dynamics and challenges.

What are the potential limitations or drawbacks of the temporally-coupled perturbation model, and how can it be further refined to better capture real-world uncertainties

One potential limitation of the temporally-coupled perturbation model is the challenge of determining the optimal value for the coupling constraint ¯ϵ. Setting ¯ϵ too large may lead to the model behaving similarly to a non-coupled attack scenario, while setting it too small could overly restrict perturbations and impact the effectiveness of the attacks. To address this limitation, the model can be further refined by incorporating adaptive mechanisms that dynamically adjust ¯ϵ based on the agent's performance and the nature of the environment. This adaptive approach can help the model strike a balance between robustness and attack effectiveness in varying scenarios.
Another drawback of the temporally-coupled perturbation model is the assumption of linear relationships between consecutive perturbations, which may not always hold in real-world environments. To enhance the model's realism, nonlinear relationships and dependencies between perturbations over time can be considered. This can involve using more sophisticated models, such as recurrent neural networks or attention mechanisms, to capture complex temporal patterns in the perturbations and improve the model's ability to handle dynamic and evolving uncertainties.

Given the focus on robustness, how can the proposed approach be combined with techniques that also optimize for sample efficiency and natural performance in the absence of adversaries

To combine the focus on robustness with techniques that optimize for sample efficiency and natural performance, the proposed approach can be integrated with methods like meta-learning and transfer learning. By leveraging meta-learning, the agent can adapt quickly to new environments and tasks, reducing the need for extensive training data and improving sample efficiency. Transfer learning can also be employed to transfer knowledge and policies learned in one setting to another, enhancing the agent's performance in diverse scenarios.
Additionally, techniques like reward shaping and curriculum learning can be used to guide the agent towards learning robust policies while maintaining natural performance. Reward shaping involves designing reward functions that incentivize desirable behaviors and penalize vulnerabilities, helping the agent learn robust strategies. Curriculum learning can gradually expose the agent to increasingly challenging scenarios, allowing it to learn in a structured manner and improve both robustness and natural performance over time. By combining these techniques with the proposed approach, the agent can achieve a balance between robustness, sample efficiency, and natural performance in the absence of adversaries.