Balancing Value Underestimation and Overestimation with Realistic Actor-Critic

• URL: http://arxiv.org/abs/2110.09712v2
• Date: Wed, 20 Oct 2021 00:59:22 GMT
• Title: Balancing Value Underestimation and Overestimation with Realistic Actor-Critic
• Authors: Sicen Li, Gang Wang, Qinyun Tang, Liquan Wang
• Abstract summary: This paper introduces a novel model-free algorithm, Realistic Actor-Critic(RAC), which can be incorporated with any off-policy RL algorithms to improve sample efficiency. RAC employs Universal Value Function Approximators (UVFA) to simultaneously learn a policy family with the same neural network, each with different trade-offs between underestimation and overestimation. We evaluate RAC on the MuJoCo benchmark, achieving 10x sample efficiency and 25% performance improvement on the most challenging Humanoid environment compared to SAC.
• Score: 6.205681604290727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model-free deep reinforcement learning (RL) has been successfully applied to challenging continuous control domains. However, poor sample efficiency prevents these methods from being widely used in real-world domains. This paper introduces a novel model-free algorithm, Realistic Actor-Critic(RAC), which can be incorporated with any off-policy RL algorithms to improve sample efficiency. RAC employs Universal Value Function Approximators (UVFA) to simultaneously learn a policy family with the same neural network, each with different trade-offs between underestimation and overestimation. To learn such policies, we introduce uncertainty punished Q-learning, which uses uncertainty from the ensembling of multiple critics to build various confidence-bounds of Q-function. We evaluate RAC on the MuJoCo benchmark, achieving 10x sample efficiency and 25% performance improvement on the most challenging Humanoid environment compared to SAC.

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