Solving OpenAI Gym problems.
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Updated
Jan 12, 2021 - Python
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.
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