  Deep Deterministic Policy Gradient (DDPG) has been proved to be a successful
reinforcement learning (RL) algorithm for continuous control tasks. However,
DDPG still suffers from data insufficiency and training inefficiency,
especially in computationally complex environments. In this paper, we propose
Asynchronous Episodic DDPG (AE-DDPG), as an expansion of DDPG, which can
achieve more effective learning with less training time required. First, we
design a modified scheme for data collection in an asynchronous fashion.
Generally, for asynchronous RL algorithms, sample efficiency or/and training
stability diminish as the degree of parallelism increases. We consider this
problem from the perspectives of both data generation and data utilization. In
detail, we re-design experience replay by introducing the idea of episodic
control so that the agent can latch on good trajectories rapidly. In addition,
we also inject a new type of noise in action space to enrich the exploration
behaviors. Experiments demonstrate that our AE-DDPG achieves higher rewards and
requires less time consuming than most popular RL algorithms in Learning to Run
task which has a computationally complex environment. Not limited to the
control tasks in computationally complex environments, AE-DDPG also achieves
higher rewards and 2- to 4-fold improvement in sample efficiency on average
compared to other variants of DDPG in MuJoCo environments. Furthermore, we
verify the effectiveness of each proposed technique component through abundant
ablation study.
