Tabular methods for reinforcement learning
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Updated
Jul 3, 2020 - Python
Tabular methods for reinforcement learning
path planning using Q learning algorithm
Demonstration of Q-Learning and SARSA algorithms utilizing Python and OpenAI GYM
This github contains a simple OpenAi Gym Maze Enviroment and (at now) a RL Algorithm to solve it.
Applying PBT optimization technique to different domains
Solutions for OpenAI Gym RL environments
Reinforcement learning algorithm implements.
Using the SARSA to beat the environment, Windy Gridworld. Implement in C++.
The following project concerns the development of an intelligent agent for the famous game produced by Nintendo Super Mario Bros. More in detail: the goal of this project was to design, implement and train an agent with the Q-learning reinforcement learning algorithm.
The implementation of some reinforcement learning techniques like (Q-learning, SARSA, DQN) in two assignments and one big project.
Pac-Man RL Agent
Temporal Difference methods - A simple implementation of SARSA algorithm applied to OpenAI gym's "CliffWalking" environment.
Implementation of SARSA algorithm for path planning
人工智能课程的实验
Two reinforcement learning algorithms (Standard SARSA Control and Tabular Dyna-Q) where an agent learns to traverse a randomly generated maze
OpenAI_gym_Taxi-v2 solved with reinforcement learning - Expected Sarsa
Implementation of an agent capable of playing a simplified version of the blackjack game using SARSA algorithm.
Open Gym Taxi v3 environment solved using sarsamax algorithm(Q-Learning)
Implementation of certain crucial algorithms in the field of reinforcement learning.
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