Multimodal-Deep-Q-Network-for-Social-Human-Robot-Interaction
by ahq1993
Multimodal Deep Q-Network (MDQN) for modelling human-like social intelligence.
AI summary
Social robot learner
A deep reinforcement learning system designed to enable robots to acquire social interaction skills through multimodal interactions with humans.
- stars
- 14
- forks
- 11
- watching
- 3
Similar projects
Found by comparing what the projects do, not just their names.
Robot teacher
An implementation of a deep reinforcement learning algorithm to teach a robot to interact with humans in a socially acceptable manner.
Reinforcement learner
An implementation of a deep reinforcement learning model for continuous control tasks
multi-agent learning protocol
This project implements a PyTorch-based framework for learning discrete communication protocols in multi-agent reinforcement learning environments.
Robot Navigation
Develops robot navigation policies in crowded spaces using reinforcement learning and attention mechanisms.
DQN
An implementation of a deep reinforcement learning architecture for playing Atari games
Social Learning Test Suite
Assesses generalization of multi-agent reinforcement learning algorithms to novel social situations
Deep learner
An implementation of a deep reinforcement learning network using PyTorch to learn human-level control through trial and error.
Robotics research project
Research on repurposing reinforcement learning for transfer between tasks in robotics and multi-step visual tasks with simulation-to-real transfer
Q learning agent
An implementation of the Q-Learning algorithm in MATLAB for training agents to navigate mazes
Grasp Quality CNN
Develops and analyzes convolutional neural networks for grasp quality in robotics
Deep learner
A PyTorch library for decentralized deep learning across the Internet.
Human-robot interaction framework
An architecture for affective human-robot interaction using multimodal recognition and emotional response models.
Distributed Training System
Automates large-scale deep learning training on distributed clusters, providing fault tolerance and fast recovery from failures.
QNA architecture
A PyTorch implementation of an improved question answering architecture with dynamic memory networks and attention mechanisms
Object descriptor network
This project presents a method for learning dense object descriptors from self-supervised data, enabling tasks such as robotic manipulation and object recognition.
