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
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
Implementation and evaluation of the RL algorithm Rainbow to learn to play Atari games.
OpenAI CartPole-v0 DeepRL-based solutions (DQN, DuelingDQN, D3QN)
Solving the custom cartpole balance problem in gazebo environment using Proximal Policy Optimization(PPO)
Implementing reinforcement learning algorithms using TensorFlow and Keras in OpenAI Gym
Agent versus Controller approach in balancing CartPole system.
PGuNN - Playing Games using Neural Networks
A few machine learning projects that I made using PyTorch
CartPole Reinforcement Learning with Neuroevolution of augmenting topologies
An implementation of the reinforcement learning for CartPole-v0 by policy optimization
CartPole-v0 solved using the REINFORCE algorithm
simple and minimal implementation of DQN using target network.
Q-Learning Agent for the CartPole environment from OpenAI Gym
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