# Dual Approximation Policy Optimization

arXiv: https://arxiv.org/abs/2410.01249 · HTML: https://mlsanity.com/paper/2410.01249/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 59 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 30 of 36)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Provably Efficient Exploration in Policy Optimization](https://mlsanity.com/paper/1912.05830/) (2019)
- [A unified view of entropy-regularized Markov decision processes](https://mlsanity.com/paper/1705.07798/) (2017)
- [Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization](https://mlsanity.com/paper/2007.06558/) (2020)
- [Neural Policy Gradient Methods: Global Optimality and Rates of Convergence](https://mlsanity.com/paper/1909.01150/) (2019)
- [Policy Mirror Descent for Reinforcement Learning: Linear Convergence, New Sampling Complexity, and Generalized Problem Classes](https://mlsanity.com/paper/2102.00135/) (2021)
- [On the Convergence Rates of Policy Gradient Methods](https://mlsanity.com/paper/2201.07443/) (2022)
- [Adaptive Trust Region Policy Optimization: Global Convergence and Faster Rates for Regularized MDPs](https://mlsanity.com/paper/1909.02769/) (2019)
- [Mirror Descent Policy Optimization](https://mlsanity.com/paper/2005.09814/) (2020)
- [PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning](https://mlsanity.com/paper/2007.08459/) (2020)
- [Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence](https://mlsanity.com/paper/2105.11066/) (2021)
- [Optimistic Policy Optimization with Bandit Feedback](https://mlsanity.com/paper/2002.08243/) (2020)
- [Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation](https://mlsanity.com/paper/2103.12923/) (2021)
- [On Linear Convergence of Policy Gradient Methods for Finite MDPs](https://mlsanity.com/paper/2007.11120/) (2020)
- [On the Linear convergence of Natural Policy Gradient Algorithm](https://mlsanity.com/paper/2105.01424/) (2021)
- [Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies](https://mlsanity.com/paper/2210.01400/) (2022)
- [Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy](https://mlsanity.com/paper/1906.10306/) (2019)
- [Mirror Descent with Relative Smoothness in Measure Spaces, with application to Sinkhorn and EM](https://mlsanity.com/paper/2206.08873/) (2022)
- [Leverage the Average: an Analysis of KL Regularization in RL](https://mlsanity.com/paper/2003.14089/) (2020)
- [Mirror Learning: A Unifying Framework of Policy Optimisation](https://mlsanity.com/paper/2201.02373/) (2022)
- [Target Network and Truncation Overcome The Deadly Triad in Q-Learning](https://mlsanity.com/paper/2203.02628/) (2022)
- [A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence](https://mlsanity.com/paper/2301.13139/) (2023)
- [Policy Optimization over General State and Action Spaces](https://mlsanity.com/paper/2211.16715/) (2022)
- [Finite-Sample Analysis of Off-Policy Natural Actor-Critic with Linear Function Approximation](https://mlsanity.com/paper/2105.12540/) (2021)

## Built on by (most cited first, 2 of 2)

- [Convergence of Policy Mirror Descent Beyond Compatible Function Approximation](https://mlsanity.com/paper/2502.11033/) (2025)
- [Policy as Data: Replay-Based Policy Dual Averaging via Advantage Regression](https://mlsanity.com/paper/2610.04638/) (2026)

## Abstract (arXiv)

We propose Dual Approximation Policy Optimization (DAPO), a framework that incorporates general function approximation into policy mirror descent methods. In contrast to the popular approach of using the L2-norm to measure function approximation errors, DAPO uses the dual Bregman divergence induced by the mirror map for policy projection. This duality framework has both theoretical and practical implications: not only does it achieve fast linear convergence with general function approximation, but it also includes several well-known practical methods as special cases, immediately providing strong convergence guarantees.