Comparative analysis of DRL algorithms on control theory environments.
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Updated
Apr 22, 2022 - Python
Comparative analysis of DRL algorithms on control theory environments.
This repository contains implementations of popular Reinforcement Learning algorithms.
Custom environment for OpenAI gym
This repository contains a re-implementation of the Proximal Policy Optimization (PPO) algorithm, originally sourced from Stable-Baselines3.
This repository is dedicated to the reinforcement learning examples. I will also upload some algorithms which are somehow correlated with RL.
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)
Implementation of several RL algorithms on the CartPole-v1 environment.
Reinforcement learning implementation for 2 very popular games namely Pong and cartpole via Deep Q learning and Policy gradient
I am trying to implement various AI algorithms on various environments (like OpenAI-gym) as I learned my toward the safe AI
Contains Expert Trajectories for various Gym Environments used for State Only Imitation Learning
Implementation of the Q-learning and SARSA algorithms to solve the CartPole-v1 environment. [Advance Machine Learning project - UniGe]
Reinforcement Learning solution to OpenAI’s Gym CartPole-v1
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