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

This method combines fuzzy logic with deep reinforcement learning (DRL) based deep Q-network (DQN). Initially, the fuzzy logic approach infers client candidates based on the stability of communication ...

The proposed scheme guarantees efficient mmWave eMBB service through an intelligent joint codebook selection and MCS adaptation scheme that exploits deep reinforcement learning (DRL), referred to as ...

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3 ...

Summary. Continuous learning is the key to having lasting influence in your career, yet a heavy workload makes it hard to find the time. To ensure you’re creating opportunities even when you ...

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 ...