This suite implements several model-free off-policy deep reinforcement learning algorithms for discrete and continuous action spaces in PyTorch. DQN Single Discrete Mnih et. al. 2015 Double DQN Single ...

Abstract: Human environments are often regulated by explicit and complex rulesets. Integrating Reinforcement Learning (RL) agents into such environments motivates the development of learning ...

Reinforcement Learning (RL) has moved beyond academic research into real-world systems that learn, adapt, and improve through interaction. Unlike traditional machine learning approaches that rely on ...

This week in Project52, I took on one of the most exciting challenges yet: a direct face-off between two of the most powerful reinforcement learning (RL) algorithms — Deep Q-Network (DQN) and Proximal ...

The container terminal is a key node in global trade and logistics, where trucks connect quay cranes, storage yards, and vessels. Optimizing truck scheduling is crucial for enhancing port efficiency ...

The study of microswimmers’ behavior, including their self-propulsion, interactions with the environment, and collective phenomena, has received significant attention over the past few decades due to ...

Hand gesture recognition (HGR) based on electromyography signals (EMGs) and inertial measurement unit signals (IMUs) has been investigated for human-machine applications in the last few years. The ...

Abstract: Modeling collective behavior is a way to better understand the mechanisms that govern collective animal behaviors. Traditional rule-based modeling methods rely heavily on human prior ...

A deep Q network (DQN) (Mnih et al., 2013) is an extension of Q learning, which is a typical deep reinforcement learning method. In DQN, a Q function expresses all action values under all states, and ...