Reinforcing Your Learning of Reinforcement Learning
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Updated
Jul 14, 2019 - Python
Reinforcing Your Learning of Reinforcement Learning
This repo implements Deep Q-Network (DQN) for solving the Frozenlake-v1 environment of the Gymnasium library using Python 3.8 and PyTorch 2.0.1 in both 4x4 and 8x8 map sizes.
An implementation of main reinforcement learning algorithms: solo-agent and ensembled versions.
Bots for Atari Games using Reinforcement Learning
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
Using the OpenAI Gym library, I implemented two reinforcement learning algorithms in the Frozen Lake environment.
Reinforcement learning algorithms to solve OpenAI gym environments
A Reinforcement Learning course with classic examples of agents trained on gym environments.
Made with the gym package from the farama foundation, this project is an hyper detailed version of the Q-Learning reinforcement on the Frozen lake's game.
Q-learning agent to solve the frozen lake problem from the OpenAI gym
Resolving the FrozenLake problem from OpenAI Gym.
Contains the implementation of SARSA, Q-learning and E-SARSA on gym environment.
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
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.
This project aims to explore the basic concepts of Reinforcement Learning using the FrozenLake environment from the OpenAI Gym library.
Solving Frozen Lake Problem with Q-learning
Coding Challenge: Build the FrozenLake Environment from OpenAI Gym using Jax
Implementation of DQN using just Numpy (SFC project)
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