FORK: A Forward-Looking Actor For Model-Free Reinforcement Learning
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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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 3.
5 papers.
- 2021A Quadratic Actor Network for Model-Free Reinforcement Learningcited by 1
- 2025Automating Curriculum Learning for Reinforcement Learning using a Skill-Based Bayesian Networkcited by 1
- 2025Multimodal Remote Inferencecited by 1
- 2022Soft Actor-Critic with Inhibitory Networks for Faster Retrainingcited by 0
- 2025D2C-HRHR: Discrete Actions with Double Distributional Critics for High-Risk-High-Return Taskscited by 0
- 2025D2C-HRHR: Discrete Actions with Double Distributional Critics for High-Risk-High-Return Tasks20 Oct 2025
- 2025Multimodal Remote Inference11 Aug 2025
- 2025Automating Curriculum Learning for Reinforcement Learning using a Skill-Based Bayesian Network21 Feb 2025
- 2022Soft Actor-Critic with Inhibitory Networks for Faster Retraining7 Feb 2022
- 2021A Quadratic Actor Network for Model-Free Reinforcement Learning11 Mar 2021