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kevin-robbkevin-robb
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Modify Q-learning (non-ros) reward function to give positive reward for reaching goal.
Now finishes in around 75-150 episodes. Ran it a few times to generate data. Did not include punishment for visiting a previously visited cell as before since it will be a different implementation than the quick map way.
1 parent 09c48bd commit 7acdaac

12 files changed

+231
-3
lines changed

‎rl-ws/data/q_test1_2020-10-30-18-41-09_c25.csv

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‎rl-ws/src/ml_long_project/src/q_nonRos.py

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#!/usr/bin/env python
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import rospy
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#import rospy
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from random import randint
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#import time
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from geometry_msgs.msg import Twist, Vector3, Point, Quaternion, Pose2D
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import numpy as np
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#from os import getcwd
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from datetime import datetime
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import pandas as pd
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#import pandas as pd
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import os
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import math
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goal_point = Pose2D(4,4,0) # Weird coord transform error
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path = []
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global_map =np.genfromtxt("/home/lelliott/turtlepath/rl-ws/map.csv", delimiter=',')
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global_map =np.genfromtxt("/home/"+getuser()+"/turtlepath/rl-ws/map.csv", delimiter=',')
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map = {}
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finished = False
@@ -257,6 +257,9 @@ def execute_rl(a,s):
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reward = -5
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if(crashed):
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reward = -10
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# give a big reward for reaching the goal
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if current_position == goal_point:
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reward = 100
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#print("Start: "+s+ " Finish: " + s_prime)
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#print(reward)
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if not (s in map): # If Q is not initalized for our action set it to 0

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