Browse > Methodology > Q-Learning

Q-Learning

71 papers with code · Methodology

The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

State-of-the-art leaderboards

No evaluation results yet. Help compare methods by submit evaluation metrics.

Latest papers with code

rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch

3 Sep 2019astooke/rlpyt

rlpyt is designed as a high-throughput code base for small- to medium-scale research in deep RL.

Q-LEARNING

591
03 Sep 2019

Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms

1 Sep 2019paintception/Deep-Quality-Value-Family-

This paper makes one step forward towards characterizing a new family of \textit{model-free} Deep Reinforcement Learning (DRL) algorithms.

Q-LEARNING

3
01 Sep 2019

Striving for Simplicity in Off-policy Deep Reinforcement Learning

10 Jul 2019google-research/batch_rl

Second, how much of the benefits of recent distributional RL algorithms is attributed to improvements in exploration versus exploitation behavior?

ATARI GAMES Q-LEARNING

1
10 Jul 2019

Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog

30 Jun 2019natashamjaques/neural_chat

Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment.

Q-LEARNING

66
30 Jun 2019

Towards Empathic Deep Q-Learning

26 Jun 2019bartbussmann/EmpathicDQN

As reinforcement learning (RL) scales to solve increasingly complex tasks, interest continues to grow in the fields of AI safety and machine ethics.

Q-LEARNING

0
26 Jun 2019

QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature Selection

International Work-Conference on Artificial Neural Networks 2019 Neofly4023/qbso-fs

In this work, we propose a hybrid metaheuristic that integrates a reinforcement learning algorithm with Bee Swarm Optimization metaheuristic (BSO) for solving feature selection problem.

FEATURE SELECTION Q-LEARNING

9
16 May 2019

Comprehensible Context-driven Text Game Playing

6 May 2019yinxusen/dqn-zork

As such, an LSTM-based DQN can take tens of days to finish the training process.

Q-LEARNING

1
06 May 2019

Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control

11 Mar 2019cts198859/deeprl_signal_control

Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power.

Q-LEARNING

23
11 Mar 2019

Deep Recurrent Q-Learning vs Deep Q-Learning on a simple Partially Observable Markov Decision Process with Minecraft

11 Mar 2019vincentberaud/Minecraft-Reinforcement-Learning

Deep Q-Learning has been successfully applied to a wide variety of tasks in the past several years.

Q-LEARNING

15
11 Mar 2019

Augmenting Learning Components for Safety in Resource Constrained Autonomous Robots

6 Feb 2019scope-lab-vu/deep-nn-car

Learning enabled components (LECs) trained using data-driven algorithms are increasingly being used in autonomous robots commonly found in factories, hospitals, and educational laboratories.

AUTONOMOUS DRIVING Q-LEARNING

4
06 Feb 2019