Skip to content

GitLab

Commit 4f810ade authored by Julian Rudolf's avatar Julian Rudolf
Browse files

agent with kill direction in state (doesnt really work)

parent 1946e957
Hide whitespace changes
Inline Side-by-side
Agent/agent.py
View file @ 4f810ade
......@@ -29,13 +29,8 @@ class QLearning:
def update(self, state, next_state, action, reward):
old_val = self.qTable[(state, action)]
self.qTable[(state, action)] = (1 - self.alpha) * self.qTable[(state, action)] + \
self.alpha * reward + \
self.alpha * self.gamma * self.max_val(next_state)
#print(state, ", ", action, " = ", old_val, "->", self.qTable[(state, action)])
#print(self.qTable[(state, action)], "->", (1 - self.alpha), "*", old_val, " + ", self.alpha, "*", reward, " + ", self.alpha, "*", self.gamma, "*", self.max_val(next_state))
#print(action)
#print("----------------------------------")
self.alpha * reward + \
self.alpha * self.gamma * self.max_val(next_state)
# calculates the max qTable value for a given state
def max_val(self, state):
......@@ -54,18 +49,19 @@ class QLearning:
best_val = -100000
best_dir = "none"
for dir in poss_actions:
#print(dir, "-> ", end='')
if (state, dir) in self.qTable:
val = self.qTable[(state, dir)]
if val > best_val:
best_dir = dir
best_val = val
else:
if (state, dir) not in self.qTable:
self.qTable[(state, dir)] = 0
val = self.qTable[(state, dir)]
#print(dir, " -> ", val)
if val > best_val:
best_dir = dir
best_val = val
if best_val == -100000:
print("new state -> random")
# print("dfl, dfr, dfu, dfd, killl, killr, killu, killd")
# print(state.df_l, state.df_r, state.df_u, state.df_d)#, " ", state.kill_l, state.kill_r, state.kill_u, state.kill_d)
# print("Action choosen: ", best_dir)
# print(self.epsilon)
# print("-----------------------------------------------------")
return best_dir
# function to choose which direction to travel
......@@ -73,10 +69,8 @@ class QLearning:
poss_actions = self.possible_directions()
rand_action = random.choice(poss_actions)
best_direction = self.best_direction(state, poss_actions)
if best_direction == "none":
best_direction = rand_action
self.epsilon = self.epsilon - 0.0001
self.epsilon = self.epsilon - 0.00005
if random.random() > self.epsilon:
#print("agent chose ", best_direction)
return best_direction
......@@ -146,8 +140,6 @@ def learning(al, ga, ep, games):
# print("Epsilon = ", qagent.epsilon)
# #render()
# print("-------------------------------------------")
# qagent.print_table()
# print(qagent.qTable)
print("Score after learing: ", score)
return qagent, q_0, poss_dirs_0
......@@ -200,6 +192,7 @@ def testing(qtable, games, q_0, poss_dirs_0, rand=False):
print("Starting testing process!")
for i in range(max_games):
playerwin = play_game_testing(qtable, q_0, poss_dirs_0, rand)
q_0, poss_dirs_0 = reset()
print("Testing Game ", i+1, "/", max_games, " finished!")
if playerwin == 1:
score[0] += 1
......@@ -238,10 +231,10 @@ def load_qtable():
alpha = 0.1
gamma = 0.5
epsilon = 0.3
rounds = 600
epsilon = 0.6
rounds = 800
agent, q0, poss_dirs0 = learning(alpha, gamma, epsilon, rounds)
save_qtable(agent.qTable)
#save_qtable(agent.qTable)
#testing(agent, 50, q0, poss_dirs0, True)
#testing(agent, 50, q0, poss_dirs0)
Agent/snake_logic.py
View file @ 4f810ade
......@@ -510,14 +510,12 @@ def step(action):
# print("Snake 1 wins")
# print(overall_score)
# print("Apples eaten: ", snake1.getscore())
reward += 100
if playerwin == 2 or (playerwin == 3 and snake2.getscore() > snake1.getscore()):
overall_score[1] += 1
# print("-------------------------------------------")
# print("Snake 2 wins")
# print(overall_score)
# print("Apples eaten: ", snake1.getscore())
reward -= 100
if playerwin == 3 and snake1.getscore() == snake2.getscore():
# print("Tie")
reward = 0
......@@ -576,8 +574,6 @@ def step(action):
playerwin = 2
if check_if_crossing(snake1.pos[0] / block_size, snake1.pos[1] / block_size):
if not init:
reward -= 1
agent_apples = []
for a, id in apples:
for apple in a:
......
Agent/state.py
View file @ 4f810ade
......@@ -258,6 +258,7 @@ def calc_state_space(snake_agent, snake_enemy, apples):
return State(dfl, dfr, dfu, dfd, killl, killr, killu, killd)
# head = (5,6)
# enemy = (8,6)
# app = ((17,28), (20,9), (17,29))
......
Game/main.py
View file @ 4f810ade
......@@ -1075,10 +1075,10 @@ def game_loop():
while apple_count > len(a):
apple = gen_rand_apple(img)
a.add(apple)
for b, img in bombs:
while bomb_count > len(b):
bomb = gen_rand_apple(img)
b.add(bomb)
# for b, img in bombs:
# while bomb_count > len(b):
# bomb = gen_rand_apple(img)
# b.add(bomb)
# initialize shield
init_shield(map_filename)
......@@ -1199,15 +1199,15 @@ def game_loop():
if agent_control and not action_taken:
if check_if_crossing(snake2.pos[0] / block_size, snake2.pos[1] / block_size):
agent_apples = []
for a, id in apples:
for a, img in apples:
for apple in a:
if id == 0:
if img == purple_apple:
agent_apples.append((int(apple.pos[0] / block_size), int(apple.pos[1] / block_size)))
state = calc_state_space(snake2, snake1, agent_apples)
# print("l: ", state.df_l, " r: ", state.df_r, " u: ", state.df_u, " d: ", state.df_d)
# print("l: ", state.kill_l, " r: ", state.kill_r, " u: ", state.kill_u, " d: ", state.kill_d)
#print("l: ", state.df_l, " r: ", state.df_r, " u: ", state.df_u, " d: ", state.df_d)
#print("l: ", state.kill_l, " r: ", state.kill_r, " u: ", state.kill_u, " d: ", state.kill_d)
action = snake2.choose_best_action(qtable, state)
print(action)
#print(action)
snake2.key_event(action)
action_taken = True
......
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment