Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples

• URL: http://arxiv.org/abs/2106.14642v5
• Date: Tue, 25 Jun 2024 07:08:34 GMT
• Title: Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples
• Authors: Li Meng, Anis Yazidi, Morten Goodwin, Paal Engelstad,
• Abstract summary: Expert Q-learning is inspired by Dueling Q-learning and aims at incorporating semi-supervised learning into reinforcement learning. An offline expert assesses the value of a state in a coarse manner using three discrete values. Our results show that Expert Q-learning is indeed useful and more resistant to the overestimation bias.
• Score: 8.938418994111716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this article, we propose a novel algorithm for deep reinforcement learning named Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning and aims at incorporating semi-supervised learning into reinforcement learning through splitting Q-values into state values and action advantages. We require that an offline expert assesses the value of a state in a coarse manner using three discrete values. An expert network is designed in addition to the Q-network, which updates each time following the regular offline minibatch update whenever the expert example buffer is not empty. Using the board game Othello, we compare our algorithm with the baseline Q-learning algorithm, which is a combination of Double Q-learning and Dueling Q-learning. Our results show that Expert Q-learning is indeed useful and more resistant to the overestimation bias. The baseline Q-learning algorithm exhibits unstable and suboptimal behavior in non-deterministic settings, whereas Expert Q-learning demonstrates more robust performance with higher scores, illustrating that our algorithm is indeed suitable to integrate state values from expert examples into Q-learning.

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