<jupyter_start><jupyter_text>**1. Import dependenceis**<jupyter_code>!pip install stable-baselines3[extra]
import os
import gym
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.evaluation import evaluate_policy<jupyter_output><empty_output><jupyter_text>**2. Load Environment**  **Understanding Environment**  **3. Train an RL model**  **4. Save and Reload model**<jupyter_code><jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10416666666666667, 'fraction_numerical': 0.016666666666666666, 'mean_word_length': 9.23404255319149, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '209699', 'license': 'MIT', 'n_tokens_mistral': 163, 'n_tokens_neox': 160, 'n_words': 35, 'path': 'Reinforcement-Learning/#01_reinforcement_learning.ipynb', 'repo_name': 'YoshlikMedia/Data-Science', 'size': '480'}