Solving OpenAI Gym problems.
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Updated
Jan 12, 2021 - Python
Solving OpenAI Gym problems.
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.
Reinforcement Learning algorithms SARSA, Q-Learning, DQN, for Classical and MuJoCo Environments and testing them with OpenAI Gym.
PyTorch implementation of GAIL and PPO reinforcement learning algorithms
Deep Reinforcement Learning by using Proximal Policy Optimization and Random Network Distillation in Tensorflow 2 and Pytorch with some explanation
solution to cartpole problem of openAI gym with different approaches
OpenAI gym CartPole using Keras
GAIL learning to imitate PPO playing CartPole.
A Policy Gradient Learning with CartPole-v0 for Siraj Raval's challenge
Implementation and evaluation of the RL algorithm Rainbow to learn to play Atari games.
Solving the custom cartpole balance problem in gazebo environment using Proximal Policy Optimization(PPO)
Q-Learning Agent for the CartPole environment from OpenAI Gym
Reinforcement Learning Projects - Breakout, Car Racing, Cart Pole
A concise PyTorch implementation of Proximal Policy Optimization(PPO) solving CartPole-v0
Component-driven library for performing DL research.
An implementation of main reinforcement learning algorithms: solo-agent and ensembled versions.
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