Solving OpenAI Gym problems.
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Updated
Jan 12, 2021 - Python
Solving OpenAI Gym problems.
Usage of genetic algorithms to train a neural network in multiple OpenAI gym environments.
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
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)
A concise PyTorch implementation of Proximal Policy Optimization(PPO) solving CartPole-v0
Q-Learning Agent for the CartPole environment from OpenAI Gym
An implementation of main reinforcement learning algorithms: solo-agent and ensembled versions.
🤖 Training an RL agent to balance a cartpole in the OpenAI Gym environment.
Implementing some RL algorithms (using PyTorch) on the CartPole environment by OpenAI.
Using DRL algorithms like Policy gradients, A2C on game environments like CartPole-v0 and other Atari games
Solving the gym cartpole v0 problem
CartPole-v0 solved using the REINFORCE algorithm
My attempt to solve the classic CartPole-v0 problem using (Deep) Reinforcement Learning
Solved CartPole-v0 with REINFORCE algorithm.
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