Add dqn agent

Author: aditya1702

.DS_Store

@@ -1,14 +1,14 @@
 # coding=utf-8
 import math
 import random
+import json
+import subprocess
 from collections import namedtuple
 
 import matplotlib
 matplotlib.use('TkAgg')
 import matplotlib.pyplot as plt
 from matplotlib import animation
-# from IPython.display import display
-# from JSAnimation.IPython_display import display_animation
 
 import numpy as np
 import torch
@@ -19,7 +19,6 @@
 from .dqn_agent_network import DqnAgentNetwork
 from .replay_memory import ReplayMemory
 from .utils import Utils
-# from moviepy.editor import ImageSequenceClip
 
 
 class DqnAgent:
@@ -76,6 +75,17 @@ def __init__(self,
 
         self.utils = Utils()
 
+        gui_code = subprocess.call(["python", "gui.py"])
+        s
+        
+        with open("./params.json", "r") as infile:
+            params_dict = json.load(infile)
+
+        self.Gamma = params_dict['Gamma']
+        self.LearningRate = params_dict['Learning Rate']
+        self.number_of_episodes = int(params_dict['Episodes'])
+        self.BatchSize = int(params_dict['Batch Size'])
+
     def train(self, rl_environment):
         """
         This method trains the agent on the game environment

Deep Reinforcement Learning/.DS_Store

@@ -0,0 +1,57 @@
+from tkinter import *
+from tkinter import messagebox
+import json
+
+fields = 'Episodes', 'Learning Rate', 'Gamma', 'Batch Size'
+
+def fetch(entries):
+   for entry in entries:
+      field = entry[0]
+      text  = entry[1].get()
+      print('%s: "%s"' % (field, text)) 
+
+def makeform(root, fields):
+   entries = []
+   for field in fields:
+      row = Frame(root)
+      lab = Label(row, width=15, text=field, anchor='w')
+      ent = Entry(row)
+      row.pack(side=TOP, fill=X, padx=5, pady=5)
+      lab.pack(side=LEFT)
+      ent.pack(side=RIGHT, expand=YES, fill=X)
+      entries.append((field, ent))
+   return entries
+
+def save_parameters_to_file(entries, root):
+   params_dict = dict()
+
+   for entry in entries:
+      field = entry[0]
+      text  = entry[1].get()
+      if field == "Episodes":
+      	if int(text) < 5:
+      		messagebox.showinfo("Error", "Too few episodes. Please enter reasonable number of episodes")
+      		return
+      	elif int(text) > 100000:
+      		messagebox.showinfo("Error", "Too many episodes. Please enter reasonable number of episodes")
+      		return
+      if text == "":
+      	messagebox.showinfo("Error", "Please enter a value for " + str(field))
+      	return
+      params_dict[field] = float(text)
+
+   with open("./params.json", "w") as outfile:
+      json.dump(params_dict, outfile)
+
+   messagebox.showinfo("Yayyy!", "The parameter values were successfully saved. Beginning training!")
+   root.destroy()
+   return
+
+root = Tk()
+ents = makeform(root, fields)
+root.title("Reinforcement Learning")
+root.bind('<Return>', (lambda event, e = ents: fetch(e)))
+
+b1 = Button(root, text = 'SUBMIT', command = (lambda e=ents: save_parameters_to_file(e, root)), bg = "blue")
+b1.pack(side = LEFT, padx = 5, pady = 5)
+root.mainloop()

Deep Reinforcement Learning/Pytorch Implementations/.DS_Store

@@ -0,0 +1 @@
+{"Episodes": 1000.0, "Learning Rate": 1.0, "Gamma": 1.0, "Batch Size": 1.0}