SARSA, Q-Learning, Expected SARSA, SARSA(λ) and Double Q-learning Implementation and Analysis
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Updated
Aug 19, 2019 - Python
SARSA, Q-Learning, Expected SARSA, SARSA(λ) and Double Q-learning Implementation and Analysis
Code for our AAMAS 2020 paper: "A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry".
A Reinforcement Learning agent to perform overtaking action using Double DQN based CNNs which takes images as input built using TensorFlow.
Pytorch implementation of Randomized Ensembled Double Q-learning (REDQ)
Implementations of various RL and Deep RL algorithms in TensorFlow, PyTorch and Keras.
With underflow, create trafic light clusters that interact together to regulate circulation
Path Planning with Reinforcement Learning algorithms in an unknown environment
This repository contains all of the Reinforcement Learning-related projects I've worked on. The projects are part of the graduate course at the University of Tehran.
Implement several deep reinforcement learning algorithms on one of games in Atari 2600 - Space Invaders.
A reinforcement learning framework for the game of Nim.
Reversi game with multiple reinforcement learning algorithms.
Solving CartPole using Distributional RL
This repository is a fork of a repository originally created by Lucas Descause. It is the codebase used for my Master's dissertation "Reinforcement Learning with Function Approximation in Continuing Tasks: Discounted Return or Average Reward?" which was also an extension of Luca's work.
Reinforcement learning algorithms
A very detailed project of Chrome Dinosaur in Deep RL for beginners
Environment-related differences of Deep Q-Learning and Deep Double Q-Learning
Use SARSA, Q learning, double Q and QV to solve a maze with Reinforcement Learning
Reinforcement Learning experiments, comparing performance of Q-learning and Double Q-learning algorithms.
Solver of the game “Coin Flip Cheaters” which can be found on https://primerlearning.org/. This is not a bot, in order to use it in the real game you would need to do it manually.
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