Abstract: This paper focuses on solving the linear quadratic regulator problem for discrete-time linear systems without knowing system matrices. The classical Q-learning methods for linear systems can ...

Implemented Behavior Cloning, DAgger, Double Q-Learning, Dueling DQN, and Proximal Policy Optimization (PPO) in a simulated environment and analyzed/compared their performance in terms of efficiency, ...

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

On Wednesday, November 22nd, OpenAI CTO Mira Murati sent a letter to employees. The letter detailed a project known internally as Q* (Pronounced Q-Star) or Q-Learning. This project was purported to be ...

It was a corporate espionage story even a real human screenwriter couldn’t have dreamed up. OpenAI, which sparked the global obsession with AI last year, found itself in the headlines with the sudden ...

When beginning to study reinforcement learning, temporal difference learning is frequently used as an entry point. In order to elaborate on this concept and demonstrate the fundamentals of ...

Abstract: Exploration and exploitation are pivot components of Q-learning, and a balance between the two components is crucial toward efficient Q-learning procedures. This paper considers Q-learning ...