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Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学
Package provides java implementation of reinforcement learning algorithms such Q-Learn, R-Learn, SARSA, Actor-Critic
SARSA, Q-Learning, Expected SARSA, SARSA(λ) and Double Q-learning Implementation and Analysis
基于强化学习(RL)的冰壶游戏实例; 梯度下降的Sarsa(lambda) + 非均匀径向基特征表示
Introduction to Reinforcement Learning in Python
Implementation of Reinforcement Algorithms from scratch
Reinforcement Learning Algorithms in a simple Gridworld
Series of Reinforcement Learning: Q-Learning, Sarsa, SarsaLambda, Deep Q Learning(DQN);一些列强化学习算法,玩OpenAI-gym游戏
Code repository with classical reinforcement learning and deep reinforcement learning methods for Pokémon battles in Pokémon Showdown.
Deep RL toy example based on gym package with several methods
Reversi game with multiple reinforcement learning algorithms.
This repository contains the codes for Term Projects as part of the Reinforcement Learning course (CS600077) that I am taking in the Autumn 2023 semester at IIT Kharagpur
Implemented Learning to Drive a bicycle using reinforcement learning and shaping.
This is a solution for the Snake AI Environment using Tabular methods
Repository of Reinforcement Learning projects done during the course @Sapienza
An agent learns the optimal path towards its goal from any starting point while avoiding obstacles.
Q-learning agent to solve the frozen lake problem from the OpenAI gym
Advanced RL algorithms for two simplified versions of chess. Shortest Path finds the minimal moves between two cells based on piece capabilities. Capture Pieces trains against random opponents aiming for maximal captures in set moves. Features Deep Q-Learning, Policy Iteration, TD and more.
Assignments and practices from various RL cources
A dummy dialog dialog system implementing traditional Reinforcement Learning algorithms like Q-Leaning and SARSA
My solution for the "Easy21" assignment of the "UCL Course on RL"
A repository covering a range of topics from multi-arm bandits to reinforcement learning algorithms. Check out different applications of bandits, MDPs and RL algorithms along with theoretical aspects.
Second homework for the Reinforcement Learning course