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

Last update: Dec 28, 2022

Overview

Reinforcement-Learning-Notebooks

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

I wrote these notebooks in March 2017 while I took the COMP 767: Reinforcement Learning [5] class by Prof. Doina Precup at McGill, Montréal. I highly recommend you to go through the class notes and references of all the papers the intructors have posted on the website.

These notebooks should be used while you read the book and go beyond the same with the referenced papers. I would suggest watching David Silver's videos and reading the book simultaneously. And when you are done with a few chapters, start implementing them. The algorithms follow a pattern and mostly are variants of each other. I have tried my best to explain each notebook's results and possible future directions.

Disclaimer: The code is a little messy. I'd written this when I was not a Pythonista. If you would like to clean them up and want to make it into a nice interface, feel free to contact me. I will be very pleased to collaborate. If you use them then please cite the source and also mention the credits as listed below. Also, email me with ways to improve, let me know if you find any bugs.

Feel free to reach me at [email protected] or see my website here

Special Credits:

[1] Denny Britz

[2] Monica Patel

[3] Sutton and Barto

[4] David Silver

[5] Doina Precup's course

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

Related tags

Overview

Reinforcement-Learning-Notebooks

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

Owner

Pulkit Khandelwal

POPPY (Physical Optics Propagation in Python) is a Python package that simulates physical optical propagation including diffraction

PyTorch implementation for Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition.

Cooperative Driving Dataset: a dataset for multi-agent driving scenarios

YOLOX-CondInst - Implement CondInst which is a instances segmentation method on YOLOX

HW3 ― GAN, ACGAN and UDA

LaneAF: Robust Multi-Lane Detection with Affinity Fields

official code for dynamic convolution decomposition

A pre-trained model with multi-exit transformer architecture.

Heat transfer problemas solved using python

Pretrained Cost Model for Distributed Constraint Optimization Problems

Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification

Code for intrusion detection system (IDS) development using CNN models and transfer learning

Implementation of Bidirectional Recurrent Independent Mechanisms (Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules)

Cobalt Strike teamserver detection.

From this paper "SESNet: A Semantically Enhanced Siamese Network for Remote Sensing Change Detection"

Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis"

House-GAN++: Generative Adversarial Layout Refinement Network towards Intelligent Computational Agent for Professional Architects

Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018

K-Means Clustering and Hierarchical Clustering Unsupervised Learning Solution in Python3.

SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data