cartpole
This project presents a reinforcement learning application using Deep Q-Learning (DQN) to train the Cartpole system. The system requires precise force applications to keep the pole balanced on a frictionless track. Details include specific hyperparameters like a learning rate of 0.001, batch size of 20, and the use of experience replay. Achieving an average reward of 195.0 over 100 trials qualifies as successful, showcasing efficient balance control. Learn more about performance metrics and see example trials of successful execution.
