# FORK: A Forward-Looking Actor For Model-Free Reinforcement Learning

arXiv: https://arxiv.org/abs/2010.01652 · HTML: https://mlsanity.com/paper/2010.01652/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [A Quadratic Actor Network for Model-Free Reinforcement Learning](https://mlsanity.com/paper/2103.06617/) (2021)
- [Automating Curriculum Learning for Reinforcement Learning using a Skill-Based Bayesian Network](https://mlsanity.com/paper/2502.15662/) (2025)
- [Multimodal Remote Inference](https://mlsanity.com/paper/2508.07555/) (2025)
- [Soft Actor-Critic with Inhibitory Networks for Faster Retraining](https://mlsanity.com/paper/2202.02918/) (2022)
- [D2C-HRHR: Discrete Actions with Double Distributional Critics for High-Risk-High-Return Tasks](https://mlsanity.com/paper/2510.17212/) (2025)

## Abstract (arXiv)

In this paper, we propose a new type of Actor, named forward-looking Actor or FORK for short, for Actor-Critic algorithms. FORK can be easily integrated into a model-free Actor-Critic algorithm. Our experiments on six Box2D and MuJoCo environments with continuous state and action spaces demonstrate significant performance improvement FORK can bring to the state-of-the-art algorithms. A variation of FORK can further solve Bipedal-WalkerHardcore in as few as four hours using a single GPU.