GRAC: Self-Guided and Self-Regularized Actor-Critic
This addresses the problem of slow learning due to target networks in deep reinforcement learning for decision-making and control tasks, representing a novel method rather than an incremental improvement.
The paper tackled the issue of target networks slowing down learning in deep reinforcement learning by introducing a self-regularized TD-learning method to prevent divergence without target networks, and a self-guided policy improvement method combining policy-gradient with zero-order optimization for robust action search, achieving or outperforming state-of-the-art results on OpenAI gym tasks.
Deep reinforcement learning (DRL) algorithms have successfully been demonstrated on a range of challenging decision making and control tasks. One dominant component of recent deep reinforcement learning algorithms is the target network which mitigates the divergence when learning the Q function. However, target networks can slow down the learning process due to delayed function updates. Our main contribution in this work is a self-regularized TD-learning method to address divergence without requiring a target network. Additionally, we propose a self-guided policy improvement method by combining policy-gradient with zero-order optimization to search for actions associated with higher Q-values in a broad neighborhood. This makes learning more robust to local noise in the Q function approximation and guides the updates of our actor network. Taken together, these components define GRAC, a novel self-guided and self-regularized actor critic algorithm. We evaluate GRAC on the suite of OpenAI gym tasks, achieving or outperforming state of the art in every environment tested.