diff --git a/codes/snake/agent.py b/codes/snake/agent.py
new file mode 100644
index 0000000..1c05b64
--- /dev/null
+++ b/codes/snake/agent.py
@@ -0,0 +1,107 @@
+import numpy as np
+import utils
+import random
+import math
+
+
+class Agent:
+
+    def __init__(self, actions, Ne, C, gamma):
+        self.actions = actions
+        self.Ne = Ne  # used in exploration function
+        self.C = C
+        self.gamma = gamma
+
+        # Create the Q and N Table to work with
+        self.Q = utils.create_q_table()
+        self.N = utils.create_q_table()
+        self.reset()
+
+    def train(self):
+        self._train = True
+
+    def eval(self):
+        self._train = False
+
+    # At the end of training save the trained model
+    def save_model(self, model_path):
+        utils.save(model_path, self.Q)
+
+    # Load the trained model for evaluation
+    def load_model(self, model_path):
+        self.Q = utils.load(model_path)
+
+    def reset(self):
+        self.points = 0
+        self.s = None
+        self.a = None
+                
+    def f(self,u,n):
+        if n < self.Ne:
+            return 1
+        return u
+
+    def R(self,points,dead):
+        if dead:
+            return -1
+        elif points > self.points:
+            return 1
+        return -0.1
+
+    def get_state(self, state):
+        # [adjoining_wall_x, adjoining_wall_y]
+        adjoining_wall_x = int(state[0] == utils.WALL_SIZE) + 2 * int(state[0] == utils.DISPLAY_SIZE - utils.WALL_SIZE)
+        adjoining_wall_y = int(state[1] == utils.WALL_SIZE) + 2 * int(state[1] == utils.DISPLAY_SIZE - utils.WALL_SIZE)
+        # [food_dir_x, food_dir_y] 
+        food_dir_x = 1 + int(state[0] < state[3]) - int(state[0] == state[3])
+        food_dir_y = 1 + int(state[1] < state[4]) - int(state[1] == state[4])
+        # [adjoining_body_top, adjoining_body_bottom, adjoining_body_left, adjoining_body_right]
+        adjoining_body = [(state[0] - body_state[0], state[1] - body_state[1]) for body_state in state[2]]
+        adjoining_body_top = int([0, utils.GRID_SIZE] in adjoining_body)
+        adjoining_body_bottom = int([0, -utils.GRID_SIZE] in adjoining_body)
+        adjoining_body_left = int([utils.GRID_SIZE, 0] in adjoining_body)
+        adjoining_body_right = int([-utils.GRID_SIZE, 0] in adjoining_body)
+        return adjoining_wall_x, adjoining_wall_y, food_dir_x, food_dir_y, adjoining_body_top, adjoining_body_bottom, adjoining_body_left, adjoining_body_right
+
+
+    def update_tables(self, _state, points, dead):
+        if self.s:
+            maxq = max(self.Q[_state]) 
+            reward = self.R(points,dead)
+            alpha = self.C / (self.C + self.N[self.s][self.a])
+            self.Q[self.s][self.a] += alpha * (reward + self.gamma * maxq - self.Q[self.s][self.a])
+            self.N[self.s][self.a] += 1.0
+        
+    def act(self, state, points, dead):
+        '''
+        :param state: a list of [snake_head_x, snake_head_y, snake_body, food_x, food_y] from environment.
+        :param points: float, the current points from environment
+        :param dead: boolean, if the snake is dead
+        :return: the index of action. 0,1,2,3 indicates up,down,left,right separately
+        TODO: write your function here.
+        Return the index of action the snake needs to take, according to the state and points known from environment.
+        Tips: you need to discretize the state to the state space defined on the webpage first.
+        (Note that [adjoining_wall_x=0, adjoining_wall_y=0] is also the case when snake runs out of the 480x480 board)
+        '''
+        
+        _state = self.get_state(state)
+        Qs = self.Q[_state][:]
+        
+        if self._train:
+            self.update_tables(_state, points, dead)
+            if dead:
+                self.reset()  
+                return
+            Ns = self.N[_state]
+            Fs = [self.f(Qs[a], Ns[a]) for a in self.actions]
+            action = np.argmax(Fs)
+            self.s = _state
+            self.a = action   
+        else:
+            if dead:
+                self.reset()  
+                return
+            action = np.argmax(Qs)
+
+        self.points = points             
+        return action
diff --git a/codes/snake/checkpoint.npy b/codes/snake/checkpoint.npy
new file mode 100644
index 0000000..591d49e
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diff --git a/codes/snake/checkpoint1.npy b/codes/snake/checkpoint1.npy
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index 0000000..84b54ca
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diff --git a/codes/snake/checkpoint2.npy b/codes/snake/checkpoint2.npy
new file mode 100644
index 0000000..4614eb7
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diff --git a/codes/snake/checkpoint3.npy b/codes/snake/checkpoint3.npy
new file mode 100644
index 0000000..20085c9
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diff --git a/codes/snake/example_assignment_and_report2.pdf b/codes/snake/example_assignment_and_report2.pdf
new file mode 100644
index 0000000..84008c0
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diff --git a/codes/snake/main.py b/codes/snake/main.py
new file mode 100644
index 0000000..c407491
--- /dev/null
+++ b/codes/snake/main.py
@@ -0,0 +1,183 @@
+import pygame
+from pygame.locals import *
+import argparse
+
+from agent import Agent
+from snake_env import SnakeEnv
+import utils
+import time
+
+class Application:
+    def __init__(self, args):
+        self.args = args
+        self.env = SnakeEnv(args.snake_head_x, args.snake_head_y, args.food_x, args.food_y)
+        self.agent = Agent(self.env.get_actions(), args.Ne, args.C, args.gamma)
+        
+    def execute(self):
+        if not self.args.human:
+            if self.args.train_eps != 0:
+                self.train()
+            self.test()
+        self.show_games()
+
+    def train(self):
+        print("Train Phase:")
+        self.agent.train()
+        window = self.args.window
+        self.points_results = []
+        first_eat = True
+        start = time.time()
+
+        for game in range(1, self.args.train_eps + 1):
+            state = self.env.get_state()
+            dead = False
+            action = self.agent.act(state, 0, dead)
+            while not dead:
+                state, points, dead = self.env.step(action)
+
+                # For debug convenience, you can check if your Q-table mathches ours for given setting of parameters
+                # (see Debug Convenience part on homework 4 web page)
+                if first_eat and points == 1:
+                    self.agent.save_model(utils.CHECKPOINT)
+                    first_eat = False
+
+                action = self.agent.act(state, points, dead)
+
+    
+            points = self.env.get_points()
+            self.points_results.append(points)
+            if game % self.args.window == 0:
+                print(
+                    "Games:", len(self.points_results) - window, "-", len(self.points_results), 
+                    "Points (Average:", sum(self.points_results[-window:])/window,
+                    "Max:", max(self.points_results[-window:]),
+                    "Min:", min(self.points_results[-window:]),")",
+                )
+            self.env.reset()
+        print("Training takes", time.time() - start, "seconds")
+        self.agent.save_model(self.args.model_name)
+
+    def test(self):
+        print("Test Phase:")
+        self.agent.eval()
+        self.agent.load_model(self.args.model_name)
+        points_results = []
+        start = time.time()
+
+        for game in range(1, self.args.test_eps + 1):
+            state = self.env.get_state()
+            dead = False
+            action = self.agent.act(state, 0, dead)
+            while not dead:
+                state, points, dead = self.env.step(action)
+                action = self.agent.act(state, points, dead)
+            points = self.env.get_points()
+            points_results.append(points)
+            self.env.reset()
+
+        print("Testing takes", time.time() - start, "seconds")
+        print("Number of Games:", len(points_results))
+        print("Average Points:", sum(points_results)/len(points_results))
+        print("Max Points:", max(points_results))
+        print("Min Points:", min(points_results))
+
+    def show_games(self):
+        print("Display Games")
+        self.env.display()
+        pygame.event.pump()
+        self.agent.eval()
+        points_results = []
+        end = False
+        for game in range(1, self.args.show_eps + 1):
+            state = self.env.get_state()
+            dead = False
+            action = self.agent.act(state, 0, dead)
+            count = 0
+            while not dead:
+                count +=1
+                pygame.event.pump()
+                keys = pygame.key.get_pressed()
+                if keys[K_ESCAPE] or self.check_quit():
+                    end = True
+                    break
+                state, points, dead = self.env.step(action)
+                # Qlearning agent
+                if not self.args.human:
+                    action = self.agent.act(state, points, dead)
+                # for human player
+                else:
+                    for event in pygame.event.get():
+                        if event.type == pygame.KEYDOWN:
+                            if event.key == pygame.K_UP:
+                                action = 2
+                            elif event.key == pygame.K_DOWN:
+                                action = 3
+                            elif event.key == pygame.K_LEFT:
+                                action = 1
+                            elif event.key == pygame.K_RIGHT:
+                                action = 0
+            if end:
+                break
+            self.env.reset()
+            points_results.append(points)
+            print("Game:", str(game)+"/"+str(self.args.show_eps), "Points:", points)
+        if len(points_results) == 0:
+            return
+        print("Average Points:", sum(points_results)/len(points_results))
+
+    def check_quit(self):
+        for event in pygame.event.get():
+            if event.type == pygame.QUIT:
+                return True
+        return False
+                
+            
+def main():
+    parser = argparse.ArgumentParser(description='CS440 MP4 Snake')
+
+    parser.add_argument('--human', default = False, action="store_true",
+                        help='making the game human playable - default False')
+
+    parser.add_argument('--model_name', dest="model_name", type=str, default="checkpoint3.npy",
+                        help='name of model to save if training or to load if evaluating - default q_agent')
+
+    parser.add_argument('--train_episodes', dest="train_eps", type=int, default=10000,
+                        help='number of training episodes - default 10000')
+
+    parser.add_argument('--test_episodes', dest="test_eps", type=int, default=1000,
+                        help='number of testing episodes - default 1000')
+
+    parser.add_argument('--show_episodes', dest="show_eps", type=int, default=10,
+                        help='number of displayed episodes - default 10')
+
+    parser.add_argument('--window', dest="window", type=int, default=100,
+                        help='number of episodes to keep running stats for during training - default 100')
+
+    parser.add_argument('--Ne', dest="Ne", type=int, default=40,
+                        help='the Ne parameter used in exploration function - default 40')
+
+    parser.add_argument('--C', dest="C", type=int, default=40,
+                        help='the C parameter used in learning rate - default 40')
+
+    parser.add_argument('--gamma', dest="gamma", type=float, default=0.2,
+                        help='the gamma paramter used in learning rate - default 0.7')
+
+    parser.add_argument('--snake_head_x', dest="snake_head_x", type=int, default=200,
+                        help='initialized x position of snake head  - default 200')
+
+    parser.add_argument('--snake_head_y', dest="snake_head_y", type=int, default=200,
+                        help='initialized y position of snake head  - default 200')
+
+    parser.add_argument('--food_x', dest="food_x", type=int, default=80,
+                        help='initialized x position of food  - default 80')
+
+    parser.add_argument('--food_y', dest="food_y", type=int, default=80,
+                        help='initialized y position of food  - default 80')
+
+
+    args = parser.parse_args()
+    app = Application(args)
+    app.execute()
+
+if __name__ == "__main__":
+    main()
diff --git a/codes/snake/q_agent.npy b/codes/snake/q_agent.npy
new file mode 100644
index 0000000..75ef415
Binary files /dev/null and b/codes/snake/q_agent.npy differ
diff --git a/codes/snake/snake_env.py b/codes/snake/snake_env.py
new file mode 100644
index 0000000..aa22809
--- /dev/null
+++ b/codes/snake/snake_env.py
@@ -0,0 +1,204 @@
+import random
+import pygame
+import utils
+
+class SnakeEnv:
+    def __init__(self, snake_head_x, snake_head_y, food_x, food_y):
+        self.game = Snake(snake_head_x, snake_head_y, food_x, food_y)
+        self.render = False
+
+    def get_actions(self):
+        return self.game.get_actions()
+
+    def reset(self):
+        return self.game.reset()
+    
+    def get_points(self):
+        return self.game.get_points()
+
+    def get_state(self):
+        return self.game.get_state()
+
+    def step(self, action):
+        state, points, dead = self.game.step(action)
+        if self.render:
+            self.draw(state, points, dead)
+        return state, points, dead
+
+    def draw(self, state, points, dead):
+        snake_head_x, snake_head_y, snake_body, food_x, food_y = state
+        self.display.fill(utils.BLUE)    
+        pygame.draw.rect( self.display, utils.BLACK,
+                [
+                    utils.GRID_SIZE,
+                    utils.GRID_SIZE,
+                    utils.DISPLAY_SIZE - utils.GRID_SIZE * 2,
+                    utils.DISPLAY_SIZE - utils.GRID_SIZE * 2
+                ])
+
+        # draw snake head
+        pygame.draw.rect(
+                    self.display, 
+                    utils.GREEN,
+                    [
+                        snake_head_x,
+                        snake_head_y,
+                        utils.GRID_SIZE,
+                        utils.GRID_SIZE
+                    ],
+                    3
+                )
+        # draw snake body
+        for seg in snake_body:
+            pygame.draw.rect(
+                self.display, 
+                utils.GREEN,
+                [
+                    seg[0],
+                    seg[1],
+                    utils.GRID_SIZE,
+                    utils.GRID_SIZE,
+                ],
+                1
+            )
+        # draw food
+        pygame.draw.rect(
+                    self.display, 
+                    utils.RED,
+                    [
+                        food_x,
+                        food_y,
+                        utils.GRID_SIZE,
+                        utils.GRID_SIZE
+                    ]
+                )
+
+        text_surface = self.font.render("Points: " + str(points), True, utils.BLACK)
+        text_rect = text_surface.get_rect()
+        text_rect.center = ((280),(25))
+        self.display.blit(text_surface, text_rect)
+        pygame.display.flip()
+        if dead:
+            # slow clock if dead
+            self.clock.tick(1)
+        else:
+            self.clock.tick(5)
+
+        return 
+
+
+    def display(self):
+        pygame.init()
+        pygame.display.set_caption('MP4: Snake')
+        self.clock = pygame.time.Clock()
+        pygame.font.init()
+
+        self.font = pygame.font.Font(pygame.font.get_default_font(), 15)
+        self.display = pygame.display.set_mode((utils.DISPLAY_SIZE, utils.DISPLAY_SIZE), pygame.HWSURFACE)
+        self.draw(self.game.get_state(), self.game.get_points(), False)
+        self.render = True
+            
+class Snake:
+    def __init__(self, snake_head_x, snake_head_y, food_x, food_y):
+        self.init_snake_head_x = snake_head_x
+        self.init_snake_head_y = snake_head_y
+        self.init_food_x = food_x
+        self.init_food_y = food_y
+        self.reset()
+
+    def reset(self):
+        self.points = 0
+        self.snake_head_x = self.init_snake_head_x
+        self.snake_head_y = self.init_snake_head_y
+        self.snake_body = []
+        self.food_x = self.init_food_x
+        self.food_y = self.init_food_y
+
+    def get_points(self):
+        return self.points
+
+    def get_actions(self):
+        return [0, 1, 2, 3]
+
+    def get_state(self):
+        return [
+            self.snake_head_x,
+            self.snake_head_y,
+            self.snake_body,
+            self.food_x,
+            self.food_y
+        ]
+
+    def move(self, action):
+        delta_x = delta_y = 0
+        if action == 0:
+            delta_x = utils.GRID_SIZE
+        elif action == 1:
+            delta_x = - utils.GRID_SIZE
+        elif action == 2:
+            delta_y = - utils.GRID_SIZE
+        elif action == 3:
+            delta_y = utils.GRID_SIZE
+
+        old_body_head = None
+        if len(self.snake_body) == 1:
+            old_body_head = self.snake_body[0]
+        self.snake_body.append((self.snake_head_x, self.snake_head_y))
+        self.snake_head_x += delta_x
+        self.snake_head_y += delta_y
+
+        if len(self.snake_body) > self.points:
+            del(self.snake_body[0])
+
+        self.handle_eatfood()
+
+        # colliding with the snake body or going backwards while its body length
+        # greater than 1
+        if len(self.snake_body) >= 1:
+            for seg in self.snake_body:
+                if self.snake_head_x == seg[0] and self.snake_head_y == seg[1]:
+                    return True
+
+        # moving towards body direction, not allowing snake to go backwards while 
+        # its body length is 1
+        if len(self.snake_body) == 1:
+            if old_body_head == (self.snake_head_x, self.snake_head_y):
+                return True
+
+        # collide with the wall
+        if (self.snake_head_x < utils.GRID_SIZE or self.snake_head_y < utils.GRID_SIZE or
+            self.snake_head_x + utils.GRID_SIZE > utils.DISPLAY_SIZE-utils.GRID_SIZE or self.snake_head_y + utils.GRID_SIZE > utils.DISPLAY_SIZE-utils.GRID_SIZE):
+            return True
+
+        return False
+
+    def step(self, action):
+        is_dead = self.move(action)
+        return self.get_state(), self.get_points(), is_dead
+
+    def handle_eatfood(self):
+        if (self.snake_head_x == self.food_x) and (self.snake_head_y == self.food_y):
+            self.random_food()
+            self.points += 1
+
+
+    def random_food(self):
+        max_x = (utils.DISPLAY_SIZE - utils.WALL_SIZE - utils.GRID_SIZE)
+        max_y = (utils.DISPLAY_SIZE - utils.WALL_SIZE - utils.GRID_SIZE)
+        
+        self.food_x = random.randint(utils.WALL_SIZE, max_x)//utils.GRID_SIZE * utils.GRID_SIZE
+        self.food_y = random.randint(utils.WALL_SIZE, max_y)//utils.GRID_SIZE * utils.GRID_SIZE
+
+        while self.check_food_on_snake():
+            self.food_x = random.randint(utils.WALL_SIZE, max_x)//utils.GRID_SIZE * utils.GRID_SIZE
+            self.food_y = random.randint(utils.WALL_SIZE, max_y)//utils.GRID_SIZE * utils.GRID_SIZE
+
+    def check_food_on_snake(self):
+        if self.food_x == self.snake_head_x and self.food_y == self.snake_head_y:
+            return True 
+        for seg in self.snake_body:
+            if self.food_x == seg[0] and self.food_y == seg[1]:
+                return True
+        return False
+        
+    
diff --git a/codes/snake/utils.py b/codes/snake/utils.py
new file mode 100644
index 0000000..01c9b00
--- /dev/null
+++ b/codes/snake/utils.py
@@ -0,0 +1,55 @@
+import numpy as np
+DISPLAY_SIZE = 560
+GRID_SIZE = 40
+WALL_SIZE = 40
+WHITE = (255, 255, 255)
+RED = (255, 0, 0)
+BLUE = (72, 61, 139)
+BLACK = (0, 0, 0)
+GREEN = (0, 255, 0)
+
+NUM_ADJOINING_WALL_X_STATES=3
+NUM_ADJOINING_WALL_Y_STATES=3
+NUM_FOOD_DIR_X=3
+NUM_FOOD_DIR_Y=3
+NUM_ADJOINING_BODY_TOP_STATES=2
+NUM_ADJOINING_BODY_BOTTOM_STATES=2
+NUM_ADJOINING_BODY_LEFT_STATES=2
+NUM_ADJOINING_BODY_RIGHT_STATES=2
+NUM_ACTIONS = 4
+
+CHECKPOINT = 'checkpoint.npy'
+
+def create_q_table():
+	return np.zeros((NUM_ADJOINING_WALL_X_STATES, NUM_ADJOINING_WALL_Y_STATES, NUM_FOOD_DIR_X, NUM_FOOD_DIR_Y,
+					 NUM_ADJOINING_BODY_TOP_STATES, NUM_ADJOINING_BODY_BOTTOM_STATES, NUM_ADJOINING_BODY_LEFT_STATES,
+					 NUM_ADJOINING_BODY_RIGHT_STATES, NUM_ACTIONS))
+
+def sanity_check(arr):
+	if (type(arr) is np.ndarray and 
+		arr.shape==(NUM_ADJOINING_WALL_X_STATES, NUM_ADJOINING_WALL_Y_STATES, NUM_FOOD_DIR_X, NUM_FOOD_DIR_Y,
+					 NUM_ADJOINING_BODY_TOP_STATES, NUM_ADJOINING_BODY_BOTTOM_STATES, NUM_ADJOINING_BODY_LEFT_STATES,
+					 NUM_ADJOINING_BODY_RIGHT_STATES,NUM_ACTIONS)):
+		return True
+	else:
+		return False
+
+def save(filename, arr):
+	if sanity_check(arr):
+		np.save(filename,arr)
+		return True
+	else:
+		print("Failed to save model")
+		return False
+
+def load(filename):
+	try:
+		arr = np.load(filename)
+		if sanity_check(arr):
+			print("Loaded model successfully")
+			return arr
+		print("Model loaded is not in the required format")
+		return None
+	except:
+		print("Filename doesnt exist")
+		return None
\ No newline at end of file