README.md

---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CartPole-v1
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: CartPole-v1
      type: CartPole-v1
    metrics:
    - type: mean_reward
      value: 500.00 +/- 0.00
      name: mean_reward
      verified: false
---

  # **Reinforce** Agent playing **CartPole-v1**
  I have used Reinforcement learning in a game, Cart Pole. The aim is to keep the equilibrium by moving left/right. While training, the game uses its results/rewards to modify its parameters to get more rewards. Specifically, the model learns what kind of tactics let the cart pole balance, and as it fails, it learns and applies those tactics to balance the cart pole. 

Some links I've found helpful include:
https://huggingface.co/learn/deep-rl-course/en/unit0/introduction#certification-process
https://colab.research.google.com/github/huggingface/deep-rl-class/blob/master/notebooks/unit4/unit4.ipynb#scrollTo=NCNvyElRStWG
https://spinningup.openai.com/en/latest/spinningup/rl_intro3.html#don-t-let-the-past-distract-you
https://stable-baselines3.readthedocs.io/en/master/guide/rl_tips.html
https://gymnasium.farama.org/content/migration-guide/
https://github.com/enerrio/CartPole-Reinforcement-Learning
https://www.ibm.com/topics/overfitting
https://learningds.org/ch/04/modeling_loss_functions.html
https://www.geeksforgeeks.org/reinforce-algorithm/