KaleabTessera
PhD Student, focusing on Multi-Agent Reinforcement Learning.
University of EdinburghEdinburgh, United Kingdom
Pinned Repositories
Baobab
Baobab is an open source multi-tenant web application designed to facilitate the application and selection process for large scale meetings within the machine learning and artificial intelligence communities globally. Supported by the Deep Learning Indaba
indaba-pracs-2022
Notebooks for the Practicals at the Deep Learning Indaba 2022.
Mava
🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX
DQN-Atari
Deep Q-Learning (DQN) implementation for Atari pong.
ICOOmen_ML_Model
IcoOmen, a machine learning model which will predict the value of an ICO token after 6 months. This uses historic data that has been aggregated from various public websites and APIs (Application programming interfaces), as well as data that has been manually collected and calculated.
KnapSackProblem
Knapsack Problem implemented in Python. This includes a Linear Greedy and Quadratic Knapsack Implementation.
Multi-Armed-Bandit
Implementation of greedy, E-greedy and Upper Confidence Bound (UCB) algorithm on the Multi-Armed-Bandit problem.
PRM-Path-Planning
Implementation of Probabilistic Roadmap Path Planning Algorithm.
Research-Paper-Reading-Template
A markdown template for taking notes to summarize research papers.
ROS-TurtleBot-Obstacle_Detection
An simple implementation of obstacle detection, using Rospy Turtlebot's depth camera and OpenCV.
KaleabTessera's Repositories
KaleabTessera/DQN-Atari
Deep Q-Learning (DQN) implementation for Atari pong.
KaleabTessera/Research-Paper-Reading-Template
A markdown template for taking notes to summarize research papers.
KaleabTessera/PRM-Path-Planning
Implementation of Probabilistic Roadmap Path Planning Algorithm.
KaleabTessera/Multi-Armed-Bandit
Implementation of greedy, E-greedy and Upper Confidence Bound (UCB) algorithm on the Multi-Armed-Bandit problem.
KaleabTessera/Gridworld-Markov-Decision-Process
Implementing a gridworld from scratch and configuring it as a Markov decision process.
KaleabTessera/Monte-Carlo-and-Temporal-Difference
Monte Carlo and Temporal Difference implementation from Chapter 5 and Chapter 6 of Reinforcement Learning: An Introduction Book by Andrew Barto and Richard S. Sutton.
KaleabTessera/Obstacle-Tower-RL
KaleabTessera/personal-site-v2
My personal website.
KaleabTessera/Policy-Gradient
Implementation of the following Policy Gradient Algorithms -Reinforce and Actor Critic.
KaleabTessera/SARSA_Cliffwalking
Implementation of SARSA for cliffwalking environment.
KaleabTessera/acme
A library of reinforcement learning components and agents
KaleabTessera/awesome-marl
A categorised list of Multi-Agent Reinforcemnt Learning (MARL) papers
KaleabTessera/BenchMARL
A collection of MARL benchmarks based on TorchRL
KaleabTessera/boostnote-markdown-cheatsheet
📋 📘 The missing one page markdown feature cheat sheet for Boostnote
KaleabTessera/botorch
Bayesian optimization in PyTorch
KaleabTessera/cleanrl
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
KaleabTessera/d2l-en
Dive into Deep Learning: an interactive deep learning book with code, math, and discussions, based on the NumPy interface.
KaleabTessera/Dynamic-Programming---OpenAI
KaleabTessera/educational
KaleabTessera/loss-landscape
Code for visualizing the loss landscape of neural nets
KaleabTessera/Mountain_Climbing_SARSA_Semi_Gradient
Implementation of SARSA Semi-Gradient Method on the Mountain Car Open AI Environment.
KaleabTessera/off-policy
PyTorch implementations of popular off-policy multi-agent reinforcement learning algorithms, including QMix, VDN, MADDPG, and MATD3.
KaleabTessera/on-policy
This is the official implementation of Multi-Agent PPO (MAPPO).
KaleabTessera/openpilot
openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for over 100 supported car makes and models.
KaleabTessera/rigl
End-to-end training of sparse deep neural networks with little-to-no performance loss.
KaleabTessera/rlax
KaleabTessera/scipy
Scipy library main repository
KaleabTessera/Shimmy
An API conversion tool for popular external reinforcement learning environments
KaleabTessera/smac
SMAC: The StarCraft Multi-Agent Challenge
KaleabTessera/SuperSuit
A collection of wrappers for Gymnasium and PettingZoo environments (being merged into gymnasium.wrappers and pettingzoo.wrappers