论文标题
叉子：无模型强化学习的前瞻性演员
FORK: A Forward-Looking Actor For Model-Free Reinforcement Learning
论文作者
论文摘要
在本文中，我们提出了一种新型的演员，称为“前瞻性演员”或“叉子”，以简称为演员批评算法。叉可以很容易地集成到无模型的参与者批评算法中。我们在具有连续状态和动作空间的六个Box2D和Mujoco环境上进行的实验表明，性能改善可以带来最新的算法。使用单个GPU，叉子的变化可以进一步求解二倍体 - walkerhardcore，只有四个小时。
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.
