Solving OpenAI Gym problems.

Deep Reinforcement Learning by using Proximal Policy Optimization and Random Network Distillation in Tensorflow 2 and Pytorch with some explanation

This Repository contains a series of google colab notebooks which I created to help people dive into deep reinforcement learning.This notebooks contain both theory and implementation of different algorithms.

Usage of genetic algorithms to train a neural network in multiple OpenAI gym environments.

PyTorch implementation of GAIL and PPO reinforcement learning algorithms

A concise PyTorch implementation of Proximal Policy Optimization(PPO) solving CartPole-v0

GAIL learning to imitate PPO playing CartPole.

solution to cartpole problem of openAI gym with different approaches

An implementation of main reinforcement learning algorithms: solo-agent and ensembled versions.

Implementation and evaluation of the RL algorithm Rainbow to learn to play Atari games.

Reinforcement Learning algorithms SARSA, Q-Learning, DQN, for Classical and MuJoCo Environments and testing them with OpenAI Gym.

Component-driven library for performing DL research.

OpenAI CartPole-v0 DeepRL-based solutions (DQN, DuelingDQN, D3QN)

Solving the custom cartpole balance problem in gazebo environment using Proximal Policy Optimization(PPO)

It is tensorflow implementation of Actor-Critic Method.

OpenAI gym CartPole using Keras

Implementing reinforcement learning algorithms using TensorFlow and Keras in OpenAI Gym

Agent versus Controller approach in balancing CartPole system.