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arXiv · 2108.10533

Entropy-Aware Model Initialization for Effective Exploration in Deep Reinforcement Learning

Sooyoung Jang·Hyung-Il Kim
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Abstract

Encouraging exploration is a critical issue in deep reinforcement learning. We investigate the effect of initial entropy that significantly influences the exploration, especially at the earlier stage. Our main observations are as follows: 1) low initial entropy increases the probability of learning failure, and 2) this initial entropy is biased towards a low value that inhibits exploration. Inspired by the investigations, we devise entropy-aware model initialization, a simple yet powerful learning strategy for effective exploration. We show that the devised learning strategy significantly reduces learning failures and enhances performance, stability, and learning speed through experiments.

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Sooyoung Jang, Hyung-Il Kim. 2021-08-24. Entropy-Aware Model Initialization for Effective Exploration in Deep Reinforcement Learning. https://doi.org/10.3390/s22155845

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