Optimizing hyperparameters of deep reinforcement learning for autonomous driving based on whale optimization algorithm.

PloS One
Nesma M AshrafM Z Rashad

Abstract

Deep Reinforcement Learning (DRL) enables agents to make decisions based on a well-designed reward function that suites a particular environment without any prior knowledge related to a given environment. The adaptation of hyperparameters has a great impact on the overall learning process and the learning processing times. Hyperparameters should be accurately estimated while training DRL algorithms, which is one of the key challenges that we attempt to address. This paper employs a swarm-based optimization algorithm, namely the Whale Optimization Algorithm (WOA), for optimizing the hyperparameters of the Deep Deterministic Policy Gradient (DDPG) algorithm to achieve the optimum control strategy in an autonomous driving control problem. DDPG is capable of handling complex environments, which contain continuous spaces for actions. To evaluate the proposed algorithm, the Open Racing Car Simulator (TORCS), a realistic autonomous driving simulation environment, was chosen to its ease of design and implementation. Using TORCS, the DDPG agent with optimized hyperparameters was compared with a DDPG agent with reference hyperparameters. The experimental results showed that the DDPG's hyperparameters optimization leads to maximizing the ...Continue Reading

References

Feb 27, 2015·Nature·Volodymyr MnihDemis Hassabis
Jan 29, 2016·Nature·David SilverDemis Hassabis
Oct 21, 2017·Nature·David SilverDemis Hassabis
Jun 25, 2019·Computational Intelligence and Neuroscience·Hardi M MohammedTarik A Rashid

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Citations

Jul 25, 2021·Sensors·Rui HeHang Zhang

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Software Mentioned

DDPG
OpenGL
DeepMind
Open Racing Car Simulator ( TORCS )
TORCS
LOTO
Open Racing Car Simulator ( TORCS
UNREAL
WOA
AlphaGo

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