Actor-Accelerated Policy Dual Averaging for Reinforcement Learning in Continuous Action Spaces
arXiv:2603.10199v1 Announce Type: new Abstract: Policy Dual Averaging (PDA) offers a principled Policy Mirror Descent (PMD) framework that more naturally admits value function approximation than standard PMD, enabling the use of approximate advantage (or Q-) functions while retaining strong convergence guarantees. However, applying PDA in continuous state and action spaces remains computationally challenging, since action selection involves solving an optimization sub-problem at each decision step. In this paper, we propose \textit{actor-accelerated PDA}, which uses a learned policy network to approximate the solution of the optimization sub-problems, yielding faster runtimes while maintaining convergence guarantees. We provide a theoretical analysis that quantifies how actor approximation error impacts the convergence of PDA under suitable assumptions. We then evaluate its performance on several benchmarks in robotics, control, and operations research problems. Actor-accelerated PDA
arXiv:2603.10199v1 Announce Type: new Abstract: Policy Dual Averaging (PDA) offers a principled Policy Mirror Descent (PMD) framework that more naturally admits value function approximation than standard PMD, enabling the use of approximate advantage (or Q-) functions while retaining strong convergence guarantees. However, applying PDA in continuous state and action spaces remains computationally challenging, since action selection involves solving an optimization sub-problem at each decision step. In this paper, we propose \textit{actor-accelerated PDA}, which uses a learned policy network to approximate the solution of the optimization sub-problems, yielding faster runtimes while maintaining convergence guarantees. We provide a theoretical analysis that quantifies how actor approximation error impacts the convergence of PDA under suitable assumptions. We then evaluate its performance on several benchmarks in robotics, control, and operations research problems. Actor-accelerated PDA achieves superior performance compared to popular on-policy baselines such as Proximal Policy Optimization (PPO). Overall, our results bridge the gap between the theoretical advantages of PDA and its practical deployment in continuous-action problems with function approximation.
Executive Summary
This article proposes Actor-Accelerated Policy Dual Averaging (AAPDA), a novel approach to reinforcement learning in continuous action spaces. By leveraging a learned policy network to approximate action selection, AAPDA achieves faster runtimes while maintaining convergence guarantees. The authors provide a theoretical analysis of the impact of actor approximation error on convergence and evaluate AAPDA on several benchmarks, demonstrating superior performance compared to popular on-policy baselines. This work bridges the gap between the theoretical advantages of Policy Dual Averaging and its practical deployment in continuous-action problems. The proposed approach has significant implications for real-world applications, particularly in robotics and control.
Key Points
- ▸ Actor-Accelerated Policy Dual Averaging (AAPDA) leverages a learned policy network for action selection in continuous action spaces.
- ▸ AAPDA maintains convergence guarantees while achieving faster runtimes compared to standard Policy Dual Averaging.
- ▸ Theoretical analysis provides insights into the impact of actor approximation error on convergence.
Merits
Improved Efficiency
AAPDA accelerates policy evaluation and action selection in continuous action spaces, reducing computational costs without compromising convergence guarantees.
Enhanced Flexibility
The proposed approach allows for the use of approximate advantage (or Q-) functions, enabling the incorporation of value function approximation into Policy Dual Averaging.
Demerits
Approximation Error
The accuracy of the learned policy network may impact the convergence of AAPDA, and the methods presented to mitigate this error are limited.
Limited Generalizability
The proposed approach may not be directly applicable to more complex domains or those with high-dimensional action spaces.
Expert Commentary
The authors have made a significant contribution to the field of reinforcement learning by bridging the gap between the theoretical advantages of Policy Dual Averaging and its practical deployment in continuous-action problems. The proposed Actor-Accelerated Policy Dual Averaging approach has the potential to accelerate the development of real-world applications, particularly in robotics and control. However, the accuracy of the learned policy network and the methods presented to mitigate approximation error are areas that require further research. Overall, this work is a valuable addition to the field and highlights the need for continued innovation in policy-based reinforcement learning methods.
Recommendations
- ✓ Future research should focus on developing more accurate and adaptable policy networks to mitigate approximation error and improve the generalizability of AAPDA.
- ✓ The authors should further investigate the implications of AAPDA on the convergence of Policy Dual Averaging and explore methods to improve the stability of the proposed approach.