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Q-Learning

91 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.

( Image credit: Playing Atari with Deep Reinforcement Learning )

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Latest papers with code

Learning State Abstractions for Transfer in Continuous Control

8 Feb 2020anonicml2019/icml_2019_state_abstraction

In this work, we answer this question in the affirmative, where we take "simple learning algorithm" to be tabular Q-Learning, the "good representations" to be a learned state abstraction, and "challenging problems" to be continuous control tasks.

CONTINUOUS CONTROL Q-LEARNING

1
08 Feb 2020

Discriminator Soft Actor Critic without Extrinsic Rewards

19 Jan 2020dnishio/DSAC

The methods based on reinforcement learning, such as inverse reinforcement learning and generative adversarial imitation learning (GAIL), can learn from only a few expert data.

IMITATION LEARNING Q-LEARNING

1
19 Jan 2020

On Solving the 2-Dimensional Greedy Shooter Problem for UAVs

2 Nov 2019LorenJAnderson/uav-2d-greedyshooter-rl

We present an approach to UAV pursuit-evasion in a 2D aerial-engagement environment using reinforcement learning (RL), a machine learning paradigm concerned with goal-oriented algorithms.

Q-LEARNING

0
02 Nov 2019

Automatic Data Augmentation by Learning the Deterministic Policy

18 Oct 2019WonderSeven/DeepAugNet

By introducing an unified optimization goal, DeepAugNet intends to combine the data augmentation and the deep model training in an end-to-end training manner which is realized by simultaneously training a hybrid architecture of dueling deep Q-learning algorithm and a surrogate deep model.

DATA AUGMENTATION Q-LEARNING

0
18 Oct 2019

Adaptive Discretization for Episodic Reinforcement Learning in Metric Spaces

17 Oct 2019seanrsinclair/AdaptiveQLearning

We present an efficient algorithm for model-free episodic reinforcement learning on large (potentially continuous) state-action spaces.

Q-LEARNING

0
17 Oct 2019

SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference

15 Oct 2019google-research/seed_rl

We present a modern scalable reinforcement learning agent called SEED (Scalable, Efficient Deep-RL).

Q-LEARNING

63
15 Oct 2019

ModelicaGym: Applying Reinforcement Learning to Modelica Models

18 Sep 2019ucuapps/modelicagym

This paper presents ModelicaGym toolbox that was developed to employ Reinforcement Learning (RL) for solving optimization and control tasks in Modelica models.

Q-LEARNING

16
18 Sep 2019

A Deep Learning Approach to Grasping the Invisible

11 Sep 2019choicelab/grasping-invisible

The target-oriented motion critic, which maps both visual observations and target information to the expected future rewards of pushing and grasping motion primitives, is learned via deep Q-learning.

Q-LEARNING

1
11 Sep 2019

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

1,298
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-DQV-Learning-

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

Q-LEARNING

26
01 Sep 2019