Kale-ab Tessera's repositories
Research-Paper-Reading-Template
A markdown template for taking notes to summarize research papers.
PRM-Path-Planning
Implementation of Probabilistic Roadmap Path Planning Algorithm.
Multi-Armed-Bandit
Implementation of greedy, E-greedy and Upper Confidence Bound (UCB) algorithm on the Multi-Armed-Bandit problem.
Gridworld-Markov-Decision-Process
Implementing a gridworld from scratch and configuring it as a Markov decision process.
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.
personal-site-v2
My personal website.
Policy-Gradient
Implementation of the following Policy Gradient Algorithms -Reinforce and Actor Critic.
SARSA_Cliffwalking
Implementation of SARSA for cliffwalking environment.
awesome-marl
A categorised list of Multi-Agent Reinforcemnt Learning (MARL) papers
boostnote-markdown-cheatsheet
📋 📘 The missing one page markdown feature cheat sheet for Boostnote
loss-landscape
Code for visualizing the loss landscape of neural nets
Mountain_Climbing_SARSA_Semi_Gradient
Implementation of SARSA Semi-Gradient Method on the Mountain Car Open AI Environment.
off-policy
PyTorch implementations of popular off-policy multi-agent reinforcement learning algorithms, including QMix, VDN, MADDPG, and MATD3.
VectorizedMultiAgentSimulator
VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.