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.
Cite
@article{arxiv.2010.01652,
title = {FORK: A Forward-Looking Actor For Model-Free Reinforcement Learning},
author = {Honghao Wei and Lei Ying},
journal= {arXiv preprint arXiv:2010.01652},
year = {2021}
}