Pinned Repositories
Bandit-and-Reinforcement-Learning
Python implementation for Reinforcement Learning algorithms -- Bandit algorithms, MDP, Dynamic Programming (value/policy iteration), Model-free Control (off-policy Monte Carlo, Q-learning)
bayesla-link-adaptation
Bayesian Link Adaptation under a BLER Target
CommPy
Digital Communication with Python
decentralized_qlearning_resource_allocation_in_wns
Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access
Using multi-agent Deep Q Learning with LSTM cells (DRQN) to train multiple users in cognitive radio to learn to share scarce resource (channels) equally without communication
DRL_for_DDBC
Simulation codes for the manuscript "Deep Reinforcement Learning for Distributed Dynamic MISO Downlink-Beamforming Coordination" submitted to IEEE Transactions on Communications
gr-ofdm
Out-of-tree module for GNU Radio containing a complete OFDM implementation including GUI for reasearch and teaching
IoT-MAB
Decentralized Intelligent Resource Allocation for LoRaWAN Networks
ITU-Challenge-ML5G-PHY-RL
Scripts for the "ITU-ML5G-PS-006: ML5G-PHY-Reinforcement learning: scheduling and resource allocation"
mabwiser
[IJAIT 2021] MABWiser: Contextual Multi-Armed Bandits Library
JCK-1096's Repositories
JCK-1096/bayesla-link-adaptation
Bayesian Link Adaptation under a BLER Target
JCK-1096/DRL_for_DDBC
Simulation codes for the manuscript "Deep Reinforcement Learning for Distributed Dynamic MISO Downlink-Beamforming Coordination" submitted to IEEE Transactions on Communications
JCK-1096/gr-ofdm
Out-of-tree module for GNU Radio containing a complete OFDM implementation including GUI for reasearch and teaching
JCK-1096/NetSoft2020-Tutorial4
JCK-1096/aes
A basic AES implementation to perform the basic operations in Rijndael's finite field, using the extended Euclidean algorithm.
JCK-1096/bandit_simulations
Bandit algorithms simulations for online learning
JCK-1096/Complete-Python-3-Bootcamp
Course Files for Complete Python 3 Bootcamp Course on Udemy
JCK-1096/Deep-Reinforcement-Learning-Explained
JCK-1096/Deep-Reinforcement-Learning-for-5G-Networks
Code for my publication: Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination. Paper accepted for publication to IEEE Transactions on Communications.
JCK-1096/Hands-On-Reinforcement-Learning-for-Games
Hands-On Reinforcement Learning for Games, published by Packt
JCK-1096/Hands-On-Reinforcement-Learning-with-Python
Hands-On Reinforcement Learning with Python, published by Packt
JCK-1096/Introduction-to-Machine-Learning
This repo will house all our course material and code snippets from the Introduction to Machine Learning Class
JCK-1096/LearningX
Deep & Classical Reinforcement Learning + Machine Learning Examples in Python
JCK-1096/markov-decision-problem
Learning about MDPs, implementing policies
JCK-1096/Markov-Decision-Processes
Implementing Markov Decision Process from scratch in Python
JCK-1096/MDP-Basics
Using MDP based models (Value Iteration and Policy Iteration) on toy environments.
JCK-1096/ML-From-Scratch
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
JCK-1096/openwifi
open-source IEEE802.11/Wi-Fi baseband chip/FPGA design
JCK-1096/Plotly-Dashboards-with-Dash
This is the repo for the Udemy Course Python Dashboards with Plotly's Dash
JCK-1096/PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
JCK-1096/Q-Learning-Algorithm
Implemented deterministic FrozenLake ‘grid world’ problem where Q-learning agent learned a defined policy to optimally navigate through the lake. Python was used to program two classes which setup the state and agent respectively. Q-values are set state-action pairs and the algorithm chooses an optimal action for the current state based on estimates of this value. The reward and next state for this action is observed which allows for the Q value to be updated. Over many epochs this algorithm can learn the best path to take for this problem as long as the strategy balances exploration and exploitation correctly.
JCK-1096/Reinforcement-Learning-1
JCK-1096/rl_notes
Notes of Reinforcement Learning MOOC by University of Alberta
JCK-1096/rlbook-exercises
JCK-1096/srsLTE
Open source SDR LTE software suite from Software Radio Systems (SRS)
JCK-1096/tau-epsilon-greedy-RL
The code for the article "(\tau,\epsilon)-GREEDY REINFORCEMENT LEARNING FOR ANTI-JAMMING WIRELESS COMMUNICATIONS"
JCK-1096/ThinkDSP
Think DSP: Digital Signal Processing in Python, by Allen B. Downey.
JCK-1096/Time-Series-Analysis
code and data for the time series analysis vids on my YouTube channel
JCK-1096/verilog-starter-tutorials
Tutorial series on verilog with code examples. Contains basic verilog code implementations and concepts.
JCK-1096/WhirlwindTourOfPython
The Jupyter Notebooks behind my OReilly report, "A Whirlwind Tour of Python"