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This project demonstrates the application of Deep Reinforcement Learning (DRL) using a Deep Q-Network (DQN) to solve custom versions of the FrozenLake environment from OpenAI Gym. The agent learns to ...

Artificial Intelligence (AI) and automation are central to Industry 4.0, driving complex decision-making, optimization, and ...

Section 4 validates the effectiveness and generality of the ND3QN algorithm through simulation experiments, comparing it with traditional RRT*, DQN, and D3QN algorithms. Section 5 concludes this work.

Abstract: This study proposes a deep Q-network (DQN) model for electric motorcycles (EMs) and a multi-agent reinforcement learning (MARL)-based central control system to support battery swapping ...

The same technique applied to DQN in a discrete action space drastically slows down learning. Our findings raise questions about the nature of on-policy and off-policy bootstrap and Monte Carlo ...