🤖 Training an RL agent to balance a cartpole in the OpenAI Gym environment.
-
Updated
Oct 3, 2023 - Python
🤖 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.
OpenAI CartPole-v0 DeepRL-based solutions (DQN, DuelingDQN, D3QN)
PGuNN - Playing Games using Neural Networks
simple and minimal implementation of DQN using target network.
A few machine learning projects that I made using PyTorch
CartPole Reinforcement Learning with Neuroevolution of augmenting topologies
OpenAI's CartPole-v0
An implementation of the reinforcement learning for CartPole-v0 by policy optimization
Solutioion to the CartPole problem using Q learning
The idea of B_Pole is that there is a pole standing up on top of a cart. The goal is to balance this pole by wiggling/moving the cart from side to side to keep the pole balanced upright. The environment is deemed successful if we can balance for 200 frames, and failure is deemed when the pole is more than 15 degrees from fully vertical.
Add a description, image, and links to the cartpole-v0 topic page so that developers can more easily learn about it.
To associate your repository with the cartpole-v0 topic, visit your repo's landing page and select "manage topics."