diff --git a/examples/gymnasium_basic_usage.py b/examples/gymnasium_basic_usage.py
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+import gymnasium as gym
+import numpy as np
+import matplotlib.pyplot as plt
+
+# For this demonstration, we'll just use the SE3 controller. 
+from rotorpy.controllers.quadrotor_control import SE3Control
+from rotorpy.vehicles.crazyflie_params import quad_params
+
+controller = SE3Control(quad_params)
+
+# 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
+
+
+# First, we need to make the gym environment. The inputs to the model are as follows...
+"""
+Inputs:
+    initial_state: the initial state of the quadrotor. The default is hover. 
+    control_mode: the appropriate control abstraction that is used by the controller, options are...
+            'cmd_motor_speeds': the controller directly commands motor speeds. 
+            'cmd_motor_thrusts': the controller commands forces for each rotor.
+            'cmd_ctbr': the controller commands a collective thrsut and body rates. 
+            'cmd_ctbm': the controller commands a collective thrust and moments on the x/y/z body axes
+            'cmd_vel': the controller commands a velocity vector in the body frame. 
+    reward_fn: the reward function, default to hover, but the user can pass in any function that is used as a reward. 
+    quad_params: the parameters for the quadrotor. 
+    max_time: the maximum time of the session. 
+    world: the world for the quadrotor to operate within. 
+    sim_rate: the simulation rate (in Hz), i.e. the timestep. 
+    render_mode: render the quadrotor.
+            'None': no rendering
+            'console': output text describing the environment. 
+            '3D': will render the quadrotor in 3D. WARNING: THIS IS SLOW. 
+
+"""
+env = gym.make("Quadrotor-v0", 
+                control_mode ='cmd_motor_speeds', 
+                reward_fn = hover_reward,
+                quad_params = quad_params,
+                max_time = 5,
+                world = None,
+                sim_rate = 100,
+                render_mode='3D')
+
+# Now reset the quadrotor.
+# Setting initial_state to 'random' will randomly place the vehicle in the map near the origin.
+# But you can also set the environment resetting to be deterministic. 
+observation, info = env.reset(initial_state='random')
+
+# Number of timesteps
+T = 300
+time = np.arange(T)*(1/100)      # Just for plotting purposes.
+position = np.zeros((T, 3))      # Just for plotting purposes. 
+velocity = np.zeros((T, 3))
+
+for i in range(T):
+
+    # Unpack the observation from the environment
+    state = {'x': observation[0:3], 'v': observation[3:6], 'q': observation[6:10], 'w': observation[10:13], 'wind': observation[13:16], 'rotor_speeds': observation[16:20]}
+    
+    # For illustrative purposes, just command the quad to hover.
+    flat = {'x': [0, 0, 0], 
+            'x_dot': [0, 0, 0], 
+            'x_ddot': [0, 0, 0], 
+            'x_dddot': [0, 0, 0],
+            'yaw': 0, 
+            'yaw_dot': 0, 
+            'yaw_ddot': 0}
+    control_dict = controller.update(0, state, flat)
+
+    # Extract the commanded motor speeds.
+    cmd_motor_speeds = control_dict['cmd_motor_speeds']
+
+    # The environment expects the control inputs to all be within the range [-1,1]
+    action = np.interp(cmd_motor_speeds, [env.unwrapped.rotor_speed_min, env.unwrapped.rotor_speed_max], [-1,1])
+
+    # Step forward in the environment
+    observation, reward, terminated, truncated, info = env.step(action)
+
+    # For plotting, save the relevant information
+    position[i, :] = observation[0:3]
+    velocity[i, :] = observation[3:6]
+
+env.close()
+
+# Plotting
+
+(fig, axes) = plt.subplots(nrows=2, ncols=1, num='Position vs Time')
+ax = axes[0]
+ax.plot(time, position[:, 0], 'r', label='X')
+ax.plot(time, position[:, 1], 'g', label='Y')
+ax.plot(time, position[:, 2], 'b', label='Z')
+ax.set_ylabel("Position, m")
+ax.legend()
+
+ax = axes[1]
+ax.plot(time, velocity[:, 0], 'r', label='X')
+ax.plot(time, velocity[:, 1], 'g', label='Y')
+ax.plot(time, velocity[:, 2], 'b', label='Z')
+ax.set_xlabel("Time, s")
+
+plt.show()
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