FORK: A FORward-looKing Actor for Model-Free Reinforcement LearningDownload PDF

28 Sept 2020 (modified: 22 Oct 2023)ICLR 2021 Conference Blind SubmissionReaders: Everyone
Keywords: Reinforcement Learning, Actor Critic, Policy Gradient, Model Free
Abstract: 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 BipedalWalkerHardcore in as few as four hours using a single GPU.
One-sentence Summary: A new type of actor named forward-looking actor or FORK for short, for Actor-Critic reinforcement learning algorithms.
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