The task contains three different agents. A random agent, a simple agent and a RL agent. The problem is similar to the Frozen Lake problem which introduced by Open AI Gym. It requires the agent to “learn” how to get across the lake from the start point to the goal point and not to fall into a hole.

Implementation of tabular Reinforcement Learning methods to navigate an agent to reach the goal through any custom N by M FrozenLake grid .

solving a simple 4*4 Gridworld almost similar to openAI gym FrozenLake using Temporal difference method Reinforcement Learning

This repository showcases the implementation of a Double Deep Q-Learnig algorithm for the FrozenLake environment from Open AI's gym library.

Project for my KTA ( IM0702-232411M) course In our project, we are tasked with learning an agent to traverse a frozen lake without falling into the water. The agent learns by trial-and-error, adjusting the actions it takes based on the rewards it received in the past.

Work-related to Agents, Multi-Agents and Reinforcement Learning

Implementation of Q-learning Algorithm on FrozenLake and Taxi environments

This repository contains various networks implementation such as MLP, Hopfield, Kohonen, ART, LVQ1, Genetic algorithms, Adaboost and fuzzy-system, CNN with python.

A Reinforcement Learning course with classic examples of agents trained on gym environments.

Part 1: 1. 4x4 grid environment 2. non-slippery environment 3. Q-learning Algorithm

An implementation and visualization of frozen lake reinforcement learning example from Open AI Gym

Reinforcement Learning practice for Taxi-v-3 and Frozen Lake environments

During my Course of Ai in kiet. I was learing reinforce learning algorithm. I have implemented Q-Learnning on Frozen Lake. Great game/ also make ppt for describe code

Modern applied deep learning with reinforcement methodology.

Implement dynamic programming to solve an AI frozen lake problem.

Resolving the FrozenLake problem from OpenAI Gym.

Contains the implementation of SARSA, Q-learning and E-SARSA on gym environment.

Reinforcement Learning Algorithms in a simple Gridworld