Reinforce-Unit4-1

Reinforce Agent playing CartPole-v1

This is a trained model of a Reinforce agent playing CartPole-v1. To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

Project Information

Policy-based learning is directly approximating π without having to learn a value function- Our objective then is to maximize the performance of the parameterized policy using gradient ascent. TL;DR: Having the cart learn to balance the pole via optimizing π for the best output; the pole not falling over. This method of learning skips over using a value function like Q-learning does, allowing an immediate improvement in the next iteration instead of having to calculate and approximate tables and numbers for a new action, as Q-learning does.

This specific CartPole model only has 500 training timesteps- the average is 1000, which is the reason why the cart struggles so much with balancing the pole in the video; it has not trained enough for it. A model trained with 1000 timesteps is successful in balancing the pole, and the more training steps a model has, the more accurate its result is, like when you play a really hard level in a video game over and over, it eventually gets easier. However, the more timesteps a model has, the longer it takes to train and render- 1000 timesteps take 10-15 minutes to load, and the time only increases the more training timesteps are inputted.

Here -https...- is a video of it working with 1000 timesteps, and here -https...- is one with 2000 (links will be inserted soon)