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befor time reward

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2wenty1ne 2025-12-09 20:38:07 +01:00
parent a965dc07ce
commit 909445135f
3 changed files with 22 additions and 4 deletions
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5
GenTunic/gen_util.py
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@ -110,3 +110,8 @@ def mutation(population, MUTATION_RATE, GEN_SIZE):
population[individual_index]["population"] = grey_to_bit(grey_to_mutate) population[individual_index]["population"] = grey_to_bit(grey_to_mutate)
return population return population
def gen_to_params():
pass

11
ReinforcmentLearning/util.py
View File

@ -129,3 +129,14 @@ def get_best_q_action(q_values, state):
def get_random_direction(): def get_random_direction():
return random.choice(list(Direction)) return random.choice(list(Direction))
def calc_time_reward(amount_iterations):
if amount_iterations < 1000:
return 10
if amount_iterations > 10000:
return 1
return - (1 / 1000) * amount_iterations + 11

8
main.py
View File

@ -1,9 +1,10 @@
from GenTunic.gen_tuning import gen_tuning_main from GenTunic.gen_tuning import gen_tuning_main
from ReinforcmentLearning.learning import multipleTries, oneTry from ReinforcmentLearning.learning import multipleTries, oneTry
from ReinforcmentLearning.util import calc_time_reward
# EPSILON = 0.1618 # EPSILON = 0.01
EPSILON = 0.01 EPSILON = 0.005
# ALPHA = 0.01 # ALPHA = 0.01
ALPHA = 0.2 ALPHA = 0.2
# GAMMA = 0.2713 # GAMMA = 0.2713
@ -18,7 +19,8 @@ REWARD_ON_LOSE = -250
plot_result = True plot_result = True
show_game = False show_game = False
print(calc_time_reward(100000))
oneTry(EPSILON, ALPHA, GAMMA, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE, plot_result, show_game) # oneTry(EPSILON, ALPHA, GAMMA, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE, plot_result, show_game)
#multipleTries(EPSILON, ALPHA, GAMMA,AMOUNT_TRIES, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE) #multipleTries(EPSILON, ALPHA, GAMMA,AMOUNT_TRIES, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE)
#gen_tuning_main(AMOUNT_TRIES, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE) #gen_tuning_main(AMOUNT_TRIES, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE)