This is a trained model of a Reinforce agent playing CartPole-v1
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Updated
Jun 26, 2024
This is a trained model of a Reinforce agent playing CartPole-v1
Contains Expert Trajectories for various Gym Environments used for State Only Imitation Learning
This is a toy implementation of a Deep Q Network for the Cartpole problem available in Gymnasium using Pytorch.
Reinforcement Learning solution to OpenAI’s Gym CartPole-v1
Implementation of several RL algorithms on the CartPole-v1 environment.
Simple implementation of Q-learning algorithm for OpenAI Gymnasium's CartPole game
This repository contains a re-implementation of the Proximal Policy Optimization (PPO) algorithm, originally sourced from Stable-Baselines3.
Applied various Reinforcement Learning (RL) algorithms to determine the optimal policy for diverse Markov Decision Processes (MDPs) specified within the OpenAI Gym library
Implementation of the Q-learning and SARSA algorithms to solve the CartPole-v1 environment. [Advance Machine Learning project - UniGe]
This repository contains implementations of popular Reinforcement Learning algorithms.
A Reinforcement Learning course with classic examples of agents trained on gym environments.
OpenAI's cartpole env solver.
Simple Muesli RL algorithm implementation (PyTorch)
Developed TD Actor-Critic and solved Grid-world, Open AI 'Lunar Lander-v2' and 'Cartpole-v1' environments.
Policy-based Deep Reinforcement Learning applied to the CartPole V1 challenge by OpenAI.
Implement RL algorithms in PyTorch and test on Gym environments.
Comparative analysis of DRL algorithms on control theory environments.
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.
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