DQN_of_DWA_matlab
DQN for DWA
An implementation of Deep Q-Learning on Dynamic Window Approach in MATLAB
learning the weight of each paras in DWA(Dynamic Window Approach) by using DQN(Deep Q-Learning)
70 stars
0 watching
20 forks
Language: Matlab
last commit: about 8 years agoLinked from 1 awesome list
Related projects:
| Repository | Description | Stars |
|---|---|---|
| An implementation of a reinforcement learning algorithm using quantile regression to model distributional behavior in agent-environment interactions. | 95 | |
| A PyTorch implementation of an improved question answering architecture with dynamic memory networks and attention mechanisms | 64 | |
| An implementation of Deep Q-Network using Caffe to train and test reinforcement learning algorithms. | 212 | |
| A MATLAB implementation demonstrating the power of deep learning in signal detection and channel estimation for OFDM systems | 119 | |
| A comprehensive collection of optimization algorithms implemented in MATLAB | 185 | |
| An implementation of a deep reinforcement learning architecture for playing Atari games | 1,828 | |
| Computes dynamic user equilibria on large-scale transportation networks using MATLAB | 67 | |
| This repository implements a deep learning-based video deblurring method using PyTorch. | 68 | |
| An implementation of a deep reinforcement learning network using PyTorch to learn human-level control through trial and error. | 387 | |
| This project uses deep learning and Lie group theory to recognize actions from skeleton data | 64 | |
| An implementation of reinforcement learning algorithm using PyTorch and designed to work with Atari games. | 96 | |
| Optimization code for topology optimization problems using machine learning and deep learning techniques | 107 | |
| This project presents a neural network model designed to answer visual questions by combining question and image features in a residual learning framework. | 39 | |
| Matlab implementation of a deep learning-based method for classifying hyperspectral images | 56 | |
| An implementation of dimensionality-driven learning with noisy labels using deep neural networks and various optimization techniques. | 58 |