Approximate Policy-Based Accelerated Deep Reinforcement Learning.

In recent years, the deep reinforcement learning (DRL) algorithms have been developed rapidly and have achieved excellent performance in many challenging tasks. However, due to the complexity of network structure and a large amount of network parameters, the training of deep network is time-consuming, and consequently, the learning efficiency of DRL is limited. In this paper, aiming to speed up the learning process of DRL agent, we propose a novel approximate policy-based accelerated (APA) algorithm from the viewpoint of the error analysis of approximate policy iteration reinforcement learning algorithms. The proposed APA is proven to be convergent even with a more aggressive learning rate, making the DRL agent have a faster learning speed. Furthermore, to combine the accelerated algorithm with deep Q-network (DQN), Double DQN and deep deterministic policy gradient (DDPG), we proposed three novel DRL algorithms: APA-DQN, APA-Double DQN, and APA-DDPG, which demonstrates the adaptability of the accelerated algorithm with DRL algorithms. We have tested the proposed algorithms on both discrete-action and continuous-action tasks. Their superior performance demonstrates their great potential in the practical applications.