Update callback function to save model

parrot_training.py

@@ -1,26 +1,21 @@
-# Continuous Action Parrot Drone Environment 3D Space Training Script
+"""
+Benchmark reinforcement learning (RL) algorithms from Stable Baselines 2.10.
+Author: Gargi Vaidya & Vishnu Saj
+- Note : Modify the RL algorithm from StableBaselines and tune the hyperparameters.
 
+"""
 import olympe
 from matplotlib import pyplot as plt
 from parrotenv import ParrotEnv
 import subprocess
 from olympe.messages.ardrone3.Piloting import TakeOff, moveBy, Landing,moveTo,PCMD
 from olympe.messages.ardrone3.PilotingState import FlyingStateChanged, AttitudeChanged, moveByChanged, AltitudeChanged, PositionChanged
-
-drone = olympe.Drone("10.202.0.1")
-drone.connection()
-command = "echo '{\"jsonrpc\": \"2.0\", \"method\": \"SetParam\", \"params\": {\"machine\":\"anafi4k\", \"object\":\"lipobattery/lipobattery\", \"parameter\":\"discharge_speed_factor\", \"value\":\"0\"}, \"id\": 1}' | curl -d @- http://localhost:8383 | python -m json.tool"
-subprocess.run(command, shell=True)
-assert drone(TakeOff()>> FlyingStateChanged(state="hovering", _timeout=5)).wait().success()
-#drone.start_piloting()
-
 import os
 import csv
 import gym
 import csv
 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
@@ -29,6 +24,13 @@
 from stable_baselines.results_plotter import load_results, ts2xy
 from stable_baselines.common.callbacks import BaseCallback
 
+# Define the drone model and and take-off
+drone = olympe.Drone("10.202.0.1")
+drone.connection()
+command = "echo '{\"jsonrpc\": \"2.0\", \"method\": \"SetParam\", \"params\": {\"machine\":\"anafi4k\", \"object\":\"lipobattery/lipobattery\", \"parameter\":\"discharge_speed_factor\", \"value\":\"0\"}, \"id\": 1}' | curl -d @- http://localhost:8383 | python -m json.tool"
+subprocess.run(command, shell=True)
+assert drone(TakeOff()>> FlyingStateChanged(state="hovering", _timeout=5)).wait().success()
+
 class SaveOnBestTrainingRewardCallback(BaseCallback):
     """
     Callback for saving a model (the check is done every ``check_freq`` steps)
@@ -72,28 +74,26 @@ def _on_step(self) -> bool:
                   self.model.save(self.save_path)
 
         return True
+# Stores a csv file for the episode reward       
 heading = ["Timestep", "Reward"]
 with    open('reward.csv', 'w', newline='') as csvFile:
         writer = csv.writer(csvFile)
         writer.writerow(heading)
         csvFile.close()
 
-# Create log dir
+# Create log directory
 log_dir = "tmp1/"
 os.makedirs(log_dir, exist_ok=True)
 
-
-
 env = ParrotEnv(destination = [0,0,1], drone= drone)
 #env.reset()
 env = Monitor(env, log_dir)
 
-#model = DQN(MlpPolicy, env, verbose=1,tensorboard_log="./dqn_lunar_tensorboard/", exploration_fraction = 0.2, learning_rate = 0.001)
-#model = TD3(MlpPolicy, env, verbose=1, learning_rate = 0.0005,tensorboard_log="./td3_parrot_tensorboard/", buffer_size = 25000)
-model = TD3.load("./tmp1/best_model.zip",env)
+model = TD3(MlpPolicy, env, verbose=1, learning_rate = 0.0005,tensorboard_log="./td3_parrot_tensorboard/", buffer_size = 25000)
 callback = SaveOnBestTrainingRewardCallback(check_freq=1000, log_dir=log_dir)
-#model = DQN(MlpPolicy, env, verbose=1)
 model.learn(total_timesteps=30000, log_interval=10,tb_log_name = "td3_run_3d", callback = callback)
 model.save("td3_parrot_3d")
+
+# Land the drone and disconnect.
 assert drone(Landing()).wait().success()
 drone.disconnection()      

parrotenv.py

@@ -152,14 +152,12 @@ def step(self, action):
     if z>5:
       z_act = min(0,action[2])
 
-    self.drone(PCMD(1, y_act, x_act, 0, z_act, timestampAndSeqNum=0, _timeout=10)>> FlyingStateChanged(state="hovering", _timeout=5)).wait()
-
-
+    self.drone(PCMD(1, y_act, x_act, 0, z_act, timestampAndSeqNum=0, _timeout=10)>> FlyingStateChanged(state="hovering", _timeout=5)).wait() 
     self.pos_feedback() # Update state of the drone in self.agent_pos
     obs = [self.agent_pos[0]-self.destination[0],self.agent_pos[1]-self.destination[1],self.agent_pos[2]-self.destination[2]]
     d = self.distance([obs[0],obs[1],obs[2]])
 
-    #Terminating Condition
+    #Terminating Condition and reward design
     done = bool(d < 0.5)
     if bool(d < 0.5):
       reward = +100