IEEE

Deep Q-Learning: Theoretical Insights From an Asymptotic Analysis

Abstract: Deep Q-learning is an important reinforcement learning algorithm, which involves training a deep neural network, called deep Q-network, to approximate the well-known Q-function. Although ...
IEEE

Action Candidate Driven Clipped Double Q-Learning for Discrete and Continuous Action Tasks

Abstract: Double Q-learning is a popular reinforcement learning algorithm in Markov decision process (MDP) problems. Clipped double Q-learning, as an effective variant of double Q-learning, employs ...
eLife

Q-learning with temporal memory to navigate turbulence

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 ...
GitHub

Create easier tutorial on using (Async)VectorEnvs

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 ...
Journal of Medical Internet Research

Optimal Treatment Selection in Sequential Systemic and Locoregional Therapy of Oropharyngeal Squamous Carcinomas: Deep Q-Learning With a Patient-Physician Digital Twin Dyad

Objective: We aim to optimize the multistep treatment of patients with head and neck cancer and predict multiple patient survival and toxicity outcomes, and we develop, apply, and evaluate a first ...
Scientific Research Publishing

Rummery, G.A. and Niranjan, M. (1994) On-Line Q-Learning Using Connectionist Systems. Department of Engineering, University of Cambridge, Cambridge.

ABSTRACT: Double Q-learning has been shown to be effective in reinforcement learning scenarios when the reward system is stochastic. We apply the idea of double learning that this algorithm uses to ...
GitHub

reinforcement_q_learning.ipynb

"\uac15\ud654 \ud559\uc2b5 (DQN) \ud29c\ud1a0\ub9ac\uc5bc\n=====\n\n**Author**: [Adam Paszke](https://github.com/apaszke), [Mark Towers](https://github.com/pseudo-rnd ...