def run():
'''
'''
RENDER_TO_SCREEN = True
# Setting up the environment
env = Environment(wrap=False,
grid_size=GRID_SIZE,
rate=80,
max_time=100,
tail=False,
food_count=1,
obstacle_count=0,
multiplier_count=0,
map_path=None,
action_space=5) #sets up the environment
if RENDER_TO_SCREEN:
env.prerender()
Q = Qmatrix(2, env) # 0 - zeros, 1 - random, 2 - textfile
# Minimise the overfitting during testing
epsilon = 0.005
# Testing for a certain amount of episodes
for episode in range(10):
state, info = env.reset()
done = False #if epsidoe is in the range of 10 it resets the environment unfo
while not done:
if RENDER_TO_SCREEN:
env.render()
if np.random.rand() <= epsilon:
action = env.sample_action(
) #if a random numpy is less than or = to epsilon then it does an action
else:
action = np.argmax(Q[env.state_index(
state)]) #else it does a different action
new_state, reward, done, info = env.step(action)
# Q[env.state_index(state), action] += alpha * (reward + gamma * np.max(Q[env.state_index(new_state)]) - Q[env.state_index(state), action])
state = new_state #gives state the value of new_state
if episode % 1 == 0:
print("Episode:", episode, "\tScore:", info["score"], "\tTime:",
info["time"]) #prints out episode, score and time
def train():
'''
Starts a function called Train
'''
RENDER_TO_SCREEN = False
# RENDER_TO_SCREEN = True
# Setting up the environment
env = Environment(wrap=False,
grid_size=GRID_SIZE,
rate=80,
max_time=100,
tail=False,
food_count=1,
obstacle_count=0,
multiplier_count=0,
map_path=None,
action_space=5)
'''
Sets the state of environemnt to equal the above grid size given, sets the speed the snake moves, the max time it runs for, if there is a tail or not, the amount of food spawned, the amount of obstacles spaned, if there is a specific path to be taken.
'''
if RENDER_TO_SCREEN:
env.prerender()
Q = Qmatrix(1, env) # 0 - zeros, 1 - random, 2 - textfile
alpha = 0.15 # Learning rate, i.e. which fraction of the Q values should be updated
gamma = 0.99 # Discount factor, i.e. to which extent the algorithm considers possible future rewards
epsilon = 0.1 # Probability to choose random action instead of best action
'''
Sets variables with values because of reasons stated above
'''
epsilon_function = True
epsilon_start = 0.8
epsilon_end = 0.05
epsilon_percentage = 0.6 # in decimal
avg_time = 0
avg_score = 0
print_episode = 1000
total_episodes = 10000
'''
Sets values to variables
'''
for episode in range(
total_episodes
): # Takes an episode and if it is in range of the total episodes it proceeds
# Reset the environment
state, info = env.reset() # Resets environment state
done = False
# Epsilon linear function
if epsilon_function:
epsilon = (
-(epsilon_start - epsilon_end) /
(epsilon_percentage * total_episodes)
) * episode + (
epsilon_start
) # minuses ep_start from ep_end dived by ep_percentage times by total_episodes then timesed by epsidoe the added to ep_start
if epsilon < epsilon_end:
epsilon = epsilon_end
#checks to see if ep is less than ep_end and if it is it makes ep = to ep_end
while not done:
# If cancelled, Q lookup table is still saved
try:
if RENDER_TO_SCREEN:
env.render()
if np.random.rand() <= epsilon:
action = env.sample_action()
else:
action = np.argmax(Q[env.state_index(state)])
#checks if new random no. is less than or = to ep, else it does a new action.
new_state, reward, done, info = env.step(action)
# print(state)
Q[env.state_index(state), action] += alpha * (
reward + gamma * np.max(Q[env.state_index(new_state)]) -
Q[env.state_index(state), action])
state = new_state #assigns new value to state
if done:
avg_time += info["time"]
avg_score += info[
"score"] # adds time nd score to the score counter and prints it out
except KeyboardInterrupt as e:
# Test to see if I can write the Q file during runtime
np.savetxt(Q_textfile_path_save,
Q.astype(np.float),
fmt='%f',
delimiter=" ")
print("Saved Q matrix to text file")
raise e
#try and except work togther, this ecept is anyresponse that doesnt fall into the try section.
if (episode % print_episode == 0 and episode != 0) or (
episode == total_episodes - 1
): #tests to see if ep mod print_ep = 0 or if ep == total_ep-1, then if it does it proceeds
print("Episode:", episode,
"\tavg t: {0:.3f}".format(avg_time / print_episode),
"\tavg score: {0:.3f}".format(avg_score / print_episode),
"\tepsilon {0:.3f}".format(
epsilon)) #prints out episodes, score, time
np.savetxt(Q_textfile_path_save,
Q.astype(np.float),
fmt='%f',
delimiter=" ")
avg_time = 0
avg_score = 0 #resets time and score to 0
# This doesn't need to be here
# np.savetxt(Q_textfile_path_save, Q.astype(np.float), fmt='%f', delimiter = " ")
print("Simulation finished. \nSaved Q matrix to text file at:",
Q_textfile_path_save)
