🤖 Training an RL agent to balance a cartpole in the OpenAI Gym environment.
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Updated
Oct 3, 2023 - Python
🤖 Training an RL agent to balance a cartpole in the OpenAI Gym environment.
Reinforcement Learning with Stable Baselines3: Train and evaluate a CartPole agent using Stable Baselines3 library. Includes code for training, saving, and testing the model, along with a GIF visualization of the trained agent.
This is an intelligent cartpole which knows how to balance itself.
Using DRL algorithms like Policy gradients, A2C on game environments like CartPole-v0 and other Atari games
Solving the gym cartpole v0 problem
Hill Climbing Algorithm implemented for the Cart Pole Environment.
My attempt to solve the classic CartPole-v0 problem using (Deep) Reinforcement Learning
Experiments on Reinforcement Learning with OpenAI Gyms
Solved CartPole-v0 with REINFORCE algorithm.
Solutioion to the CartPole problem using Q learning
Using Double Q Networks with experience replay to solve Cartpole v0 in just 184 episodes, implemented in Tensorflow 2.
OpenAI's CartPole-v0
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.
Google DeepMind "Playing Atari with Deep Reinforcement Learning" paper inspired implementation to solve cart pole problem
one of my ai homeworks, playing with cartpole in python gym
A CartpoleV0 solution using Keras DNN. A version of the PRO trained model is included also.
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