Supervised Q-Learning can be a Strong Baseline for Continuous Control

Hao Sun, Ziping Xu, Taiyi Wang, Meng Fang, Bolei Zhou

Keywords: Zeroth-Order Method, Continuous Control, Supervised Optimization

Abstract:

Policy gradient (PG) algorithms have been widely used in reinforcement learning (RL). However, PG algorithms rely on exploiting the value function being learned with the first-order update locally, which results in limited sample efficiency. In this work, we propose an alternative method called Zeroth-Order Supervised Policy Improvement (ZOSPI). ZOSPI exploits the estimated value function $Q$ globally while preserving the local exploitation of the PG methods based on zeroth-order policy optimization. This learning paradigm follows Q-learning but overcomes the difficulty of efficiently operating argmax in continuous action space.
It finds max-valued action within a small number of samples. The policy learning of ZOSPI has two steps: First, it samples actions and evaluates those actions with a learned value estimator, and then it learns to perform the action with the highest value through supervised learning. We further demonstrate such a supervised learning framework can learn multi-modal policies. Experiments show that ZOSPI achieves competitive results on the continuous control benchmarks with a remarkable sample efficiency.

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TL;DR: We propose to use Zeroth-Order supervised policy optimization based on Q-learning as alternative to policy gradient in continuous control tasks.