This repository contains implementations of popular Reinforcement Learning algorithms.
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Updated
Jun 8, 2023 - Jupyter Notebook
This repository contains implementations of popular Reinforcement Learning algorithms.
Comparative analysis of DRL algorithms on control theory environments.
Simple implementation of Q-learning algorithm for OpenAI Gymnasium's CartPole game
Developed TD Actor-Critic and solved Grid-world, Open AI 'Lunar Lander-v2' and 'Cartpole-v1' environments.
This is a toy implementation of a Deep Q Network for the Cartpole problem available in Gymnasium using Pytorch.
This program implemented CNN and Q Learning strategies for predicting the best left/right move for gym API CartPole-v1, and the goal is to achieve 200 frames before the pole fall down.
Deep Q Learning applied to the CartPole V1 challenge by OpenAI. The problem is solved both in the naive and the vision scenarios, the latter by exploiting game frames and CNN.
Simple Muesli RL algorithm implementation (PyTorch)
Reinforcement learning implementation for 2 very popular games namely Pong and cartpole via Deep Q learning and Policy gradient
Contains Expert Trajectories for various Gym Environments used for State Only Imitation Learning
Solving OpenAI Gym
Un semplicissimo Agente IA per Gym di OpenAI
A q-learning approach to the cartpole environment.
Solving the CartPole-v1 problem using Deep Q-Learning
Train agent for solving the CartPole environment using OpenAI gym and Keras-Tensorflow library
Reinforcement Learning with Gym and Pytorch for Atari Games
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