Add custom waypoints

parrot_predict.py

@@ -1,58 +1,65 @@
-# # Script to load and run the trained TD3 model
+"""
+Benchmark reinforcement learning (RL) algorithms from Stable Baselines 2.10.
+Author: Gargi Vaidya & Vishnu Saj
+- Note : 
 
+"""
 import olympe
 from parrotenv import ParrotEnv
 from olympe.messages.ardrone3.Piloting import TakeOff, moveBy, Landing,moveTo
-from olympe.messages.ardrone3.PilotingState import FlyingStateChanged, AttitudeChanged, moveByChanged, AltitudeChanged, PositionChanged
-
+from olympe.messages.ardrone3.PilotingState import FlyingStateChanged, 
 import os
 import gym
 import numpy as np
 import matplotlib.pyplot as plt
-
 from stable_baselines import TD3
 from stable_baselines.td3.policies import MlpPolicy
 from stable_baselines.common.vec_env import DummyVecEnv
 
 drone = olympe.Drone("10.202.0.1")
 drone.connection()
 assert drone(TakeOff()>> FlyingStateChanged(state="hovering", _timeout=5)).wait().success()
-#drone.start_piloting()
 
+# Define the waypoints
 A=[3,-3,3]
 B=[3,3,5]
 C=[-3,3,2]
 D=[-3,-3,3]
 obs=[0,0,0]
-model = TD3.load("./tmp/best_model.zip")
 
+# Load the trained RL model
+model = TD3.load("./tmp/best_model.zip")
 
+# Evaluate model from origin state to waypoint A
 env = ParrotEnv(destination = A, drone= drone)
 done = 0
 while not done:
     action, _states = model.predict(obs)
     obs, rewards, done, info = env.step(action)
     env.render()
+# Evaluate model from origin state to waypoint B
 env = ParrotEnv(destination = B, drone= drone)
 done = 0
 while not done:
     action, _states = model.predict(obs)
     obs, rewards, done, info = env.step(action)
     env.render()
+# Evaluate model from origin state to waypoint C
 env = ParrotEnv(destination = C, drone= drone)
 done = 0
 while not done:
     action, _states = model.predict(obs)
     obs, rewards, done, info = env.step(action)
     env.render()
+# Evaluate model from origin state to waypoint A
 env = ParrotEnv(destination = D, drone= drone)
 done = 0
 while not done:
     action, _states = model.predict(obs)
     obs, rewards, done, info = env.step(action)
     env.render()
 
-#drone.start_piloting()
+# Land the drone
 assert drone(Landing()).wait().success()
 drone.disconnection()