# Policy Search in Continuous Action Domains: an Overview

arXiv: https://arxiv.org/abs/1803.04706 · HTML: https://mlsanity.com/paper/1803.04706/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2018.

## Built on by (most cited first, 10 of 10)

- [Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey](https://mlsanity.com/paper/2110.01411/) (2021)
- [Combining Evolution and Deep Reinforcement Learning for Policy Search: a Survey](https://mlsanity.com/paper/2203.14009/) (2022)
- [Sparse Reward Exploration via Novelty Search and Emitters](https://mlsanity.com/paper/2102.03140/) (2021)
- [On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning](https://mlsanity.com/paper/2104.14534/) (2021)
- [Analysis and Optimisation of Bellman Residual Errors with Neural Function Approximation](https://mlsanity.com/paper/2106.08774/) (2021)
- [Policy Search with Rare Significant Events: Choosing the Right Partner to Cooperate with](https://mlsanity.com/paper/2103.06846/) (2021)
- [Zeroth-Order Actor-Critic: An Evolutionary Framework for Sequential Decision Problems](https://mlsanity.com/paper/2201.12518/) (2022)
- [Random Actions vs Random Policies: Bootstrapping Model-Based Direct Policy Search](https://mlsanity.com/paper/2210.11801/) (2022)
- [SHIRO: Soft Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2212.12786/) (2022)
- [Hierarchical Reinforcement Learning with Targeted Causal Interventions](https://mlsanity.com/paper/2507.04373/) (2025)

## Abstract (arXiv)

Continuous action policy search is currently the focus of intensive research, driven both by the recent success of deep reinforcement learning algorithms and the emergence of competitors based on evolutionary algorithms. In this paper, we present a broad survey of policy search methods, providing a unified perspective on very different approaches, including also Bayesian Optimization and directed exploration methods. The main message of this overview is in the relationship between the families of methods, but we also outline some factors underlying sample efficiency properties of the various approaches.