Removed first PPO script

examples/ppo_hover.py

@@ -1,87 +0,0 @@
-import gymnasium as gym
-import numpy as np
-import matplotlib.pyplot as plt
-import os
-
-from rotorpy.vehicles.crazyflie_params import quad_params  # Import quad params for the quadrotor environment.
-
-# Import the QuadrotorEnv gymnasium environment using the following command.
-from rotorpy.learning.quadrotor_environments import QuadrotorEnv
-
-# Reward functions can be specified by the user, or we can import from existing reward functions.
-from rotorpy.learning.quadrotor_reward_functions import hover_reward
-
-"""
-In this script, we demonstrate how to train a hovering control policy in RotorPy using Proximal Policy Optimization. 
-We use our custom quadrotor environment for Gymnasium along with stable baselines for the PPO implementation. 
-
-The task is for the quadrotor to stabilize to hover at the origin when starting at a random position nearby. 
-
-"""
-
-# First we'll set up some directories for saving the policy and logs.
-
-models_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "rotorpy", "learning", "policies")
-log_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "rotorpy", "learning", "logs")
-if not os.path.exists(models_dir):
-    os.makedirs(models_dir)
-if not os.path.exists(log_dir):
-    os.makedirs(log_dir)
-
-# Next import Stable Baselines.
-try:
-    import stable_baselines3
-except:
-    raise ImportError('You must have Stable Baselines installed via pip install stable_baselines3')
-
-from stable_baselines3 import PPO                                   # We'll use PPO for training.
-from stable_baselines3.ppo.policies import MlpPolicy                # The policy will be represented by an MLP
-from stable_baselines3.common.evaluation import evaluate_policy     # For evaluation
-
-# Make the environment. For this demo we'll train a policy in cmd_vel. Higher abstractions lead to easier tasks.
-env = gym.make("Quadrotor-v0", 
-                control_mode ='cmd_ctbr', 
-                reward_fn = hover_reward,
-                quad_params = quad_params,
-                max_time = 5,
-                world = None,
-                sim_rate = 100,
-                render_mode='3D')
-
-# from stable_baselines3.common.env_checker import check_env
-# check_env(env, warn=True)  # you can check the environment using built in tools
-
-# Reset the environment
-observation, info = env.reset(initial_state='random')
-
-# Load the policy. 
-model_path = os.path.join(models_dir, "PPOMlpPolicy.zip")
-# If model is already trained, load it and continue training
-if os.path.exists(model_path):
-    model = PPO.load(model_path, env=env, tensorboard_log=log_dir)
-    print("Loading existing model.")
-else:
-    model = PPO(MlpPolicy, env, verbose=1, tensorboard_log=log_dir)
-
-# Training... 
-num_timesteps = 10_000
-num_episodes = 100
-
-best_mean_reward = -100_000_000  # Really large number so that in the first iteration it is overwritten
-
-for i in range(num_episodes):
-
-    # This line will run num_timesteps for training and log the results every so often.
-    model.learn(total_timesteps=num_timesteps, reset_num_timesteps=False, tb_log_name="PPO_{}".format(i))
-
-    if i % 10 == 0:  # Evaluate the policy every 10 episodes
-        mean_reward, std_reward = evaluate_policy(model, env, n_eval_episodes=100)
-
-    # Save the model if it is better than the previous one
-    if mean_reward >= best_mean_reward:
-        best_mean_reward = mean_reward
-        # Example for saving best model
-        print("Saving new best model")
-        model.save(model_path)
-    else:
-        print("Not saving model, mean reward was {:.2f}, but best reward is {:.2f}".format(mean_reward, best_mean_reward))