Action Robust Reinforcement Learning and Applications in Continuous Control (1901.09184v2)

Abstract: A policy is said to be robust if it maximizes the reward while considering a bad, or even adversarial, model. In this work we formalize two new criteria of robustness to action uncertainty. Specifically, we consider two scenarios in which the agent attempts to perform an action $a$, and (i) with probability $\alpha$, an alternative adversarial action $\bar a$ is taken, or (ii) an adversary adds a perturbation to the selected action in the case of continuous action space. We show that our criteria are related to common forms of uncertainty in robotics domains, such as the occurrence of abrupt forces, and suggest algorithms in the tabular case. Building on the suggested algorithms, we generalize our approach to deep reinforcement learning (DRL) and provide extensive experiments in the various MuJoCo domains. Our experiments show that not only does our approach produce robust policies, but it also improves the performance in the absence of perturbations. This generalization indicates that action-robustness can be thought of as implicit regularization in RL problems.

• The paper introduces two robust RL models—Probabilistic Action Robust MDP and Noisy Action Robust MDP—that address uncertainties with adversarial actions and perturbations.
• It demonstrates that robust policies outperform baselines in MuJoCo environments, showing improved performance even without external disturbances.
• The robust criteria act as implicit regularizers that simplify policy design for continuous control, paving the way for adaptive, real-world applications.

Analysis of Action Robust Reinforcement Learning and Applications in Continuous Control

The paper "Action Robust Reinforcement Learning and Applications in Continuous Control" presents a novel approach to enhance robustness in reinforcement learning (RL) policies given action uncertainties. It introduces two criteria—Probabilistic Action Robust MDP (PR-MDP) and Noisy Action Robust MDP (NR-MDP)—and explores their implementation in continuous control environments using deep reinforcement learning (DRL).

Summary of Contributions

The authors have successfully formalized two distinct robustness criteria addressing action uncertainties:

1. Probabilistic Action Robust MDP (PR-MDP): This model assumes that, with a probability $\alpha$, an adversarial action is executed instead of the intended action. It reflects situations where sudden, unexpected disruptions may occur.
2. Noisy Action Robust MDP (NR-MDP): Here, an adversary adds a perturbation to the selected action. This is analogous to continuous control scenarios where the action execution is imprecise due to consistent disturbances.

Both models extend existing frameworks to include implicit regularization and devise algorithms initially in the tabular scenario, which are then scaled to deep reinforcement learning environments.

Key Results

The experiments conducted on various MuJoCo environments demonstrate the efficacy and robustness of the proposed methods:

• MuJoCo Experiments: The generalized approach for DRL in these environments, with alternating control between model uncertainty and adversarial action selection, led to robust policy formation. Notably, these robust policies also performed better in scenarios absent of perturbations, suggesting an implicit regularization effect.
• Robustness vs. Performance: In several tests, such as Hopper-v2 and Humanoid, the policies developed using the proposed robustness criteria outperformed the baseline models, thus exhibiting greater resilience to action uncertainties across scenarios.

Implications and Theoretical Insights

This research has significant implications for the development of robust RL systems, particularly in applications involving continuous control systems such as robotics:

• Action Robustness as Implicit Regularization: The benefits observed even in non-perturbed environments propose a new angle for regularizing RL to avoid overfitting and improve generalization.
• Simplified Robust Policy Development: Unlike traditional robust RL methods requiring a predefined uncertainty set, the approach here introduces a singular parameter, $\alpha$, to encapsulate uncertainty, thus simplifying the implementation process.

However, certain limitations persist. The assumption of an equivalent deterministic optimal policy for NR-MDP may not hold in practice, potentially creating discrepancies between theory and application. Moreover, while hyperparameter stability is demonstrated, identifying optimal parameter settings for NR-MDP remains challenging across varying environments.

Speculation on Future Developments

The exploration of action robust policies in RL frameworks opens various directions for future research:

• Dynamic Adjustment of $\alpha$: Developing adaptive mechanisms for $\alpha$ based on real-time feedback could enhance policy robustness across dynamically changing environments.
• Extended Applications: Implementing these principles beyond MuJoCo in broader contexts like autonomous driving or drone control could yield insights into complex, real-world RL applications.
• Convergence Analysis and Error Sensitivity: More robust convergence proofs under approximation errors could provide deeper assurance of the proposed methods in practical scenarios where environmental dynamics are approximated.

Overall, this research advances the theory and practice of robust reinforcement learning, with significant potential for real-world applications where model uncertainties and dynamic disturbances are commonplace.