Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control

• URL: http://arxiv.org/abs/2207.13730v1
• Date: Wed, 27 Jul 2022 18:11:04 GMT
• Title: Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control
• Authors: Takuya Kanazawa, Haiyan Wang, Chetan Gupta
• Abstract summary: Uncertainty quantification is one of the central challenges for machine learning in real-world applications. Disentangling and evaluating uncertainties simultaneously stands a chance of improving the agent's final performance. We propose an uncertainty-aware reinforcement learning algorithm for continuous control tasks.
• Score: 13.767812547998735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncertainty quantification is one of the central challenges for machine learning in real-world applications. In reinforcement learning, an agent confronts two kinds of uncertainty, called epistemic uncertainty and aleatoric uncertainty. Disentangling and evaluating these uncertainties simultaneously stands a chance of improving the agent's final performance, accelerating training, and facilitating quality assurance after deployment. In this work, we propose an uncertainty-aware reinforcement learning algorithm for continuous control tasks that extends the Deep Deterministic Policy Gradient algorithm (DDPG). It exploits epistemic uncertainty to accelerate exploration and aleatoric uncertainty to learn a risk-sensitive policy. We conduct numerical experiments showing that our variant of DDPG outperforms vanilla DDPG without uncertainty estimation in benchmark tasks on robotic control and power-grid optimization.

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