diff --git a/DQN.py b/DQN.py
index 9376d0c..136fe1e 100644
--- a/DQN.py
+++ b/DQN.py
@@ -1,3 +1,7 @@
+# Modified version of
+# DQN implementation by Tejas Kulkarni found at
+# https://github.com/mrkulk/deepQN_tensorflow
+
 import numpy as np
 import tensorflow as tf
 
diff --git a/alphaAgents.py b/alphaAgents.py
index e2f8c78..5a230c3 100644
--- a/alphaAgents.py
+++ b/alphaAgents.py
@@ -1,3 +1,13 @@
+# Used code from
+# DQN implementation by Tejas Kulkarni found at
+# https://github.com/mrkulk/deepQN_tensorflow
+
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
 import numpy as np
 import random
 import util
diff --git a/database.py b/database.py
deleted file mode 100644
index 55eebbf..0000000
--- a/database.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import numpy as np
-import gc
-import time
-
-class database:
-	def __init__(self, size, input_dims):
-		#create database with input_dims as list of input dimensions
-		self.size = size
-		self.states = np.zeros([self.size,7,7],dtype='float') #image dimensions
-		self.actions = np.zeros(self.size,dtype='float')
-		self.terminals = np.zeros(self.size,dtype='float')
-		#self.nextstates = np.zeros([self.size,input_dims[0],input_dims[1],input_dims[2]],dtype='float')
-		self.rewards = np.zeros(self.size,dtype='float')
-
-		self.counter = 0 #keep track of next empty state
-		self.batch_counter = 0
-		self.rand_idxs = np.arange(3,300)
-		self.flag = False
-		return
-
-	def get_four(self,idx):
-		four_s = np.zeros([7,7,10])
-		four_n = np.zeros([7,7,10])
-		for i in range(0,10):
-			four_s[:,:,i] = self.states[idx-3+i]
-			four_n[:,:,i] = self.states[idx-2+i]
-
-		return four_s,self.actions[idx],self.terminals[idx],four_n,self.rewards[idx]
-
-	def get_batches(self, bat_size):
-		bat_s = np.zeros([bat_size,7,7,10])
-		bat_a = np.zeros([bat_size])
-		bat_t = np.zeros([bat_size])
-		bat_n = np.zeros([bat_size,7,7,10])
-		bat_r = np.zeros([bat_size])
-		ss = time.time()
-		for i in range(bat_size):
-			if self.batch_counter >= len(self.rand_idxs) - bat_size :
-				self.rand_idxs = np.arange(3,self.get_size()-1)
-				np.random.shuffle(self.rand_idxs)
-				self.batch_counter = 0
-			s,a,t,n,r = self.get_four(self.rand_idxs[self.batch_counter])
-			bat_s[i] = s; bat_a[i] = a; bat_t[i] = t; bat_n[i] = n; bat_r[i] = r
-			self.batch_counter += 1
-
-		e3 = time.time()-ss
-		return bat_s,bat_a,bat_t,bat_n,bat_r
-
-	def insert(self, prevstate_proc,reward,action,terminal):
-		self.states[self.counter] = prevstate_proc
-		#self.nextstates[self.counter] = newstate_proc
-		self.rewards[self.counter] = reward
-		self.actions[self.counter] = action
-		self.terminals[self.counter] = terminal
-
-		#update counter
-		self.counter += 1
-		if self.counter >= self.size:
-			self.flag = True
-			self.counter = 0
-		return
-
-	def get_size(self):
-		if self.flag == False:
-			return self.counter
-		else:
-			return self.size
-