Zeroth-Order Actor-Critic

• URL: http://arxiv.org/abs/2201.12518v1
• Date: Sat, 29 Jan 2022 07:09:03 GMT
• Title: Zeroth-Order Actor-Critic
• Authors: Yuheng Lei, Jianyu Chen, Shengbo Eben Li, Sifa Zheng
• Abstract summary: We propose Zeroth-Order Actor-Critic algorithm (ZOAC) that unifies these two methods into an on-policy actor-critic architecture. We evaluate our proposed method on a range of challenging continuous control benchmarks using different types of policies, where ZOAC outperforms zeroth-order and first-order baseline algorithms.
• Score: 6.5158195776494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zeroth-order optimization methods and policy gradient based first-order methods are two promising alternatives to solve reinforcement learning (RL) problems with complementary advantages. The former work with arbitrary policies, drive state-dependent and temporally-extended exploration, possess robustness-seeking property, but suffer from high sample complexity, while the latter are more sample efficient but restricted to differentiable policies and the learned policies are less robust. We propose Zeroth-Order Actor-Critic algorithm (ZOAC) that unifies these two methods into an on-policy actor-critic architecture to preserve the advantages from both. ZOAC conducts rollouts collection with timestep-wise perturbation in parameter space, first-order policy evaluation (PEV) and zeroth-order policy improvement (PIM) alternately in each iteration. We evaluate our proposed method on a range of challenging continuous control benchmarks using different types of policies, where ZOAC outperforms zeroth-order and first-order baseline algorithms.

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