Abstract: Q-learning is arguably one of the most applied representative reinforcement learning approaches and one of the off-policy strategies. Since the emergence of Q-learning, many studies have ...

This important study uses reinforcement learning to study how turbulent odor stimuli should be processed to yield successful navigation. The authors find that there is an optimal memory length over ...

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 ...

Despite the fact that insight is a crucial component of creative thought, the means by which it is cultivated remain unknown. The effects of learning traits on insight, specifically, has not been the ...

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 ...

We employ deep reinforcement learning (RL) to train an agent to successfully translate a high-frequency trading signal into a trading strategy that places individual limit orders. Based on the ABIDES ...

Create a more basic tutorial on using (Async)VectorEnvs and why you should learn them. I would say that perhaps taking the already excellent blackjact_agent tutorial and rewriting is using AsyncEnvs ...

This project is the bachelor thesis of Noah Ruhmer. It compares dynamic programming to reinforcement-learning approaches by the example of a simple control problem.