OpenAI's cartpole env solver.

Proximal Policy Optimization(PPO) with Intrinsic Curiosity Module(ICM)

Experiments of the three PPO-Algorithms (PPO, clipped PPO, PPO with KL-penalty) proposed by John Schulman et al. on the 'Cartpole-v1' environment.

Solving CartPole-v1 environment in Keras with Actor Critic algorithm an Deep Reinforcement Learning algorithm

Stabilizing an Inverted Pendulum on a cart using Deep Reinforcement Learning

A Complete Collection of Deep RL Famous Algorithms implemented in Gymnasium most Popular environments

Implement RL algorithms in PyTorch and test on Gym environments.

Implementation of the Q-learning and SARSA algorithms to solve the CartPole-v1 environment. [Advance Machine Learning project - UniGe]

Implementation of several RL algorithms on the CartPole-v1 environment.

Deep Q-Network (DQN) for CartPole game from OpenAI gym

Custom environment for OpenAI gym

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.

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

Solving CartPole-v1 environment in Keras with Advantage Actor Critic (A2C) algorithm an Deep Reinforcement Learning algorithm

I am trying to implement various AI algorithms on various environments (like OpenAI-gym) as I learned my toward the safe AI

PGuNN - Playing Games using Neural Networks

Vanilla Actor Critic

Applied various Reinforcement Learning (RL) algorithms to determine the optimal policy for diverse Markov Decision Processes (MDPs) specified within the OpenAI Gym library

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.