Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning
This addresses a specific problem in reinforcement learning for continuous action spaces, offering an incremental improvement in efficiency and generalization.
The paper tackles the inefficiency and poor generalization of model-free deep reinforcement learning in large-scale continuous action spaces by proposing a method that learns action representations in latent spaces, dividing learning into unsupervised representation and small-scale policy models, and demonstrates effectiveness on MountainCar, CarRacing, and Cheetah experiments.
Deep reinforcement learning (DRL) breaks through the bottlenecks of traditional reinforcement learning (RL) with the help of the perception capability of deep learning and has been widely applied in real-world problems.While model-free RL, as a class of efficient DRL methods, performs the learning of state representations simultaneously with policy learning in an end-to-end manner when facing large-scale continuous state and action spaces. However, training such a large policy model requires a large number of trajectory samples and training time. On the other hand, the learned policy often fails to generalize to large-scale action spaces, especially for the continuous action spaces. To address this issue, in this paper we propose an efficient policy learning method in latent state and action spaces. More specifically, we extend the idea of state representations to action representations for better policy generalization capability. Meanwhile, we divide the whole learning task into learning with the large-scale representation models in an unsupervised manner and learning with the small-scale policy model in the RL manner.The small policy model facilitates policy learning, while not sacrificing generalization and expressiveness via the large representation model. Finally,the effectiveness of the proposed method is demonstrated by MountainCar,CarRacing and Cheetah experiments.