diff --git a/examples/ppo_hover_eval.py b/examples/ppo_hover_eval.py
new file mode 100644
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+++ b/examples/ppo_hover_eval.py
@@ -0,0 +1,76 @@
+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 evaluate the policy trained in ppo_hover_train.py.  
+
+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")
+
+# Next import Stable Baselines.
+try:
+    import stable_baselines3
+except:
+    raise ImportError('To run this example 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
+
+num_cpu = 4   # for parallelization
+
+# Choose the weights for our reward function. Here we are creating a lambda function over hover_reward.
+reward_function = lambda obs, act: hover_reward(obs, act, weights={'x': 1, 'v': 0.1, 'w': 0, 'u': 1e-5})
+
+# 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 = reward_function,
+                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', options={'pos_bound': 2, 'vel_bound': 0})
+
+# Print out policies for the user to select.
+print("Select one of the models:")
+models_available = os.listdir(models_dir)
+for i, name in enumerate(models_available):
+    print(f"{i}: {name}")
+model_idx = int(input("Enter the model index: "))    
+
+# Load the model
+model_path = os.path.join(models_dir, models_available[model_idx])
+print(f"Loading model from the path {model_path}")
+model = PPO.load(model_path, env=env, tensorboard_log=log_dir)
+
+num_episodes = 10
+for i in range(num_episodes):
+    obs, info = env.reset()
+    terminated = False
+    while not terminated:
+        env.render()
+        action, _ = model.predict(obs)
+        obs, reward, terminated, truncated, info = env.step(action)
+
+plt.show()
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