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

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

solution to cartpole problem of openAI gym with different approaches

GAIL learning to imitate PPO playing CartPole.

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.

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

Component-driven library for performing DL research.

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

It is tensorflow implementation of Actor-Critic Method.

Agent versus Controller approach in balancing CartPole system.

OpenAI gym CartPole using Keras

A Policy Gradient Learning with CartPole-v0 for Siraj Raval's challenge