Adversarial Constrained Policy Optimization: Improving Constrained Reinforcement Learning by Adapting Budgets (2410.20786v1)

Abstract: Constrained reinforcement learning has achieved promising progress in safety-critical fields where both rewards and constraints are considered. However, constrained reinforcement learning methods face challenges in striking the right balance between task performance and constraint satisfaction and it is prone for them to get stuck in over-conservative or constraint violating local minima. In this paper, we propose Adversarial Constrained Policy Optimization (ACPO), which enables simultaneous optimization of reward and the adaptation of cost budgets during training. Our approach divides original constrained problem into two adversarial stages that are solved alternately, and the policy update performance of our algorithm can be theoretically guaranteed. We validate our method through experiments conducted on Safety Gymnasium and quadruped locomotion tasks. Results demonstrate that our algorithm achieves better performances compared to commonly used baselines.

• The paper introduces ACPO, a novel dynamic budget adaptation method that balances reward maximization and constraint satisfaction in reinforcement learning.
• It employs two adversarial stages that alternate between optimizing rewards and minimizing constraint costs without relying on fixed budgets.
• Empirical tests in Safety Gymnasium and quadruped locomotion tasks confirm ACPO’s superior performance compared to traditional fixed-budget approaches.

Adversarial Constrained Policy Optimization: A Closer Examination

The paper "Adversarial Constrained Policy Optimization" by Jianming Ma, Jingtian Ji, and Yue Gao offers a novel approach to enhancing constrained reinforcement learning (CRL) through adaptive budget management. Constrained reinforcement learning frameworks address complex problems where both task performance and constraint satisfaction are critical, making them particularly relevant in safety-critical applications.

Core Contributions and Methodology

The authors propose the Adversarial Constrained Policy Optimization (ACPO) algorithm, which reframes the constrained RL problem into two adversarial stages. These stages alternate between reinforcing reward maximization under current constraints and minimizing constraint costs while preserving significant reward gains. Unlike existing CRL methodologies where fixed cost budgets may stifle exploration or lead to sub-optimal, conservative solutions, ACPO adapts the cost budgets dynamically. The authors' algorithm is theoretically grounded with performance guarantees through derived lower performance bounds of policy updates.

The first stage focuses on the reward maximization given the current cost budget. This approach balances optimizing the reward while ensuring constraints are managed within a defined cost budget. Conversely, the second, adversarial stage minimizes costs under a reward budget, encouraging policy adaptation and exploration beyond local minima. These alternating iterations avoid reliance on predefined budget trajectories, unlike curriculum learning-based approaches which often depend on expert knowledge. The methodology is notable for its adaptability and potential to overcome typical limitations of fixed budget constraints, allowing for intelligent budget recalibrations during training.

Numerical Results and Comparisons

The paper demonstrates ACPO's effectiveness with empirical results from Safety Gymnasium environments and quadruped locomotion tasks. Across various scenarios, ACPO consistently achieves improved rewards while meeting constraint conditions compared to other contemporary baseline algorithms. For instance, in tasks like CarGoal1, it yielded superior performance with higher reward returns under set cost budgets.

In multi-constraint environments, such as quadruped locomotion tasks, ACPO achieves efficacy improvements by dynamically adjusting multiple constraints, showcasing its ability to scale to complex real-world tasks. This adaptability allows ACPO to harness a broader search space in policy space exploration, efficiently achieving Pareto-optimal solutions that balance reward and constraint satisfaction.

Theoretical and Practical Implications

The theoretical implications of this work extend CRL frameworks by introducing a robust mechanism for dynamically adjusting constraints without resorting to static or expert-driven methods. The adversarial nature of the ACPO stages provides enhanced exploration capabilities while ensuring convergence towards a balanced policy that matches real-world safety requirements.

Practically, the ACPO framework can be foundational for developing AI that operates in dynamic environments where safety and performance are both essential. Its capability to automatically adapt costs and rewards has potential translational value for various domains, from autonomous driving to industrial automation, where unexpected conditions require rapid adjustments without compromising safety standards.

Speculation on Future Directions

Future research directions may include extending ACPO to handle high-dimensional state spaces with partial observability or implementing ACPO in varied multi-agent systems where inter-agent constraints also need consideration. Moreover, further investigations could integrate multi-modal sensory inputs to enhance decision-making processes and broaden the adaptability and robustness of ACPO across diverse and unforeseen environments.

In summary, "Adversarial Constrained Policy Optimization" contributes significantly to constrained reinforcement learning by introducing a novel adversarial approach for dynamically optimizing both reward and constraint satisfaction. The detailed exploration of alternating adversarial stages coupled with strong empirical and theoretical foundations enhances the state-of-the-art in safe reinforcement learning, poised for application across numerous safety-critical operational fields.