Deep Recurrent Deterministic Policy Gradient for Physical Control

The observable states play a significant role in Reinforcement Learning (RL), meanwhile, the performance of RL is strongly associated with the quality of inferred hidden states. It is a challenging task to accurately extract hidden states because they are often related to both environment's and agent's histories, and require numerous domain knowledge. In this work, we aim to leverage history information to improve the performance of agent. Firstly, we discuss that the neglect and usual process of history information are harmful to agent's performance. Secondly, we propose a novel model that combines the advantage of both supervised learning and RL. Specifically, we extend the framework of classical policy gradient and propose to extract history information using recurrent neural networks. Thirdly, we evaluate our model in simulated physical control environments, outperforming the state-of-the-art models and performing obviously better on more challenging tasks. Finally, we analyze the reasons and suggest possible approaches to extend and scale up the model.