shubhampachori12110095 Goto Github PK

reinforcement-learning-2

Minimal and Clean Reinforcement Learning Examples

reinforcement-learning-3

Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions. The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP.

reinforcement-learning-agents

Play various of games using deep learning Tensorflow and deep Evolution Strategies

reinforcement-learning-algorithms-and-dynamic-programming

Reinforcement learning Algorithms such as SARSA, Q learning, Actor-Critic Policy Gradient and Value Function Approximation were applied to stabilize an inverted pendulum system and achieve optimal control. So essentially, the concept of Reinforcement Learning Controllers has been established. The Reinforcement Learning Controllers have been compared on the basis of performance and efficiency and they are separately compared with the classical Linear Quadratic Regulator Controller. Each of the RL controller have been integrated with a Swing up controller. A virtual switch toggles between the Swing up controller and the RL controller automatically, based on the value of the angular deviation theta with respect to the vertical plane. My research paper and my undergraduate thesis have been uploaded for reference. All the codes have also been uploaded.

reinforcement-learning-an-introduction

Python implementation of Reinforcement Learning: An Introduction

reinforcement-learning-based-encoder-decoder-implementation-

Implementation of the paper "A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules"

reinforcement-learning-cvrp

Dynamic Attention Encoder-Decoder model to learn and design heuristics to solve capacitated vehicle routing problems

reinforcement-learning-in-portfolio-management-

This project implements three deep reinforcement learning algorithms, including PG (for finance), DDPG and PPO, on portfolio management

reinforcement-learning-kdd

reinforcement-learning-notebooks

A collection of Reinforcement Learning algorithms from Sutton and Barto's book and other research papers implemented in Python.

reinforcement-learning-with-tensorflow

Simple Reinforcement learning tutorials

reinforcement_learning_ai_video_games

Code for each week's short video of Siraj Raval Course on Reinforcement Learning "AI for Video Games"

reinforcement_learning_tsp

Using a Reinforcement Learning algorithm to solve the Travelling Salesman Problem.

relation-autoencoder

this is the code used in the paper "Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations"

relation-network

keras implementation of [A simple neural network module for relational reasoning](https://arxiv.org/pdf/1706.01427.pdf)

relation-network-1

Tensorflow Implementation of Relation Networks for the bAbI QA Task, detailed in "A Simple Neural Network Module for Relational Reasoning," [https://arxiv.org/abs/1706.01427] by Santoro et. al.

relation-network-2

Tensorflow implementation of Relation Network (bAbI dataset)

relation-network-babi

tensorflow implementation of “A simple neural network module for relational reasoning” for babi dataset

relation-networks-for-object-detection

Relation Networks for Object Detection

relation_extraction

Relation Extraction using Deep learning(CNN)

relational-gcn

Keras-based implementation of Relational Graph Convolutional Networks

relational-networks

Pytorch implementation of "A simple neural network module for relational reasoning" (Relational Networks)

relational-rnn-pytorch

An implementation of DeepMind's Relational Recurrent Neural Networks in PyTorch.

relationclassification-rl

Reinforcement Learning for Relation Classification from Noisy Data(AAAI2018)

relativeconucb

Implementation of SIGIR 2021 paper: Comparison-based Conversational Recommender System with Relative Bandit Feedback

relaynet_pytorch

Pytorch Implementation of retinal OCT Layer Segmentation (with trained models)

relevant-search-book

Code and Examples for Relevant Search

remi

"Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions", ACM Multimedia 2020

renderforcnn

Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views

rendernet

Code for RenderNet: A deep convolutional network for differentiable rendering from 3D shapes