def run_agent(env, tests_moment, gamma=1, theta=1e-8):
global _ENVIROMENT_CLASS
_ENVIROMENT_CLASS = enviroment_choose.env_choose(env)
tmp = policy_iteration(env, gamma=gamma, theta=theta)
agent_info = {
"policy": tmp[0],
"state_action_table": tmp[1]
}
'''
TESTING
'''
#Ottengo dall'ambiente i tipi di test che mi puo' restituire
type_test_list = _ENVIROMENT_CLASS.type_test()
tests_result = []
tmp_tests_result = {}
n_test = 100
n_episodes_test = 100
for type_test in type_test_list:
tmp_tests_result.update({type_test: []})
for _ in tqdm(range(n_test)):
test_iteration_i = {}
for type_test in type_test_list:
test_iteration_i.update({type_test: 0})
#Per ogni test eseguiamo 100 "episodi"
for _ in range(n_episodes_test):
done = False
state = _ENVIROMENT_CLASS.reset_env(env)
while not done:
action = np.argmax(agent_info["policy"][state]) # Use the best learned action
test_dict = _ENVIROMENT_CLASS.test_policy(env, action)
state = test_dict["env_info"]["next_state"]
done = test_dict["env_info"]["done"]
for type_test in type_test_list:
test_iteration_i[type_test] += test_dict[type_test]
for type_test in type_test_list:
test_iteration_i[type_test] = test_iteration_i[type_test] / n_episodes_test
tests_result.append(test_iteration_i)
for type_test in tmp_tests_result:
for test in tests_result:
tmp_tests_result[type_test].append(test[type_test])
return {"agent_info": agent_info, "tests_result": tmp_tests_result}
def run_agent(env,
tests_moment,
n_games,
n_episodes,
alpha=0.1,
gamma=0.6,
epsilon=0.1,
n_step=10,
lambd=0.92):
global _ENVIROMENT_CLASS
global _ENV
global _N_GAMES
global _N_EPISODES
global _ALPHA
global _GAMMA
global _EPSILON
global _LAMBDA
global _N_STEP
global _ESTIMATOR
global _TESTS_MOMENT
_ENVIROMENT_CLASS = enviroment_choose.env_choose(env)
_ENV = env
_N_GAMES = n_games
_N_EPISODES = n_episodes
_ALPHA = alpha
_GAMMA = gamma
_EPSILON = epsilon
_LAMBDA = lambd
_N_STEP = n_step
_ESTIMATOR = QEstimator(env=_ENV, step_size=_ALPHA, \
num_tilings=_ENVIROMENT_CLASS.num_tilings(), \
max_size=_ENVIROMENT_CLASS.IHT_max_size(), trace=True)
_TESTS_MOMENT = tests_moment
results = sarsa_lambda()
tests_result_dict = {}
for type_test in _TYPE_TEST_LIST:
tests_result_dict.update({type_test: []})
for type_test in tests_result_dict:
for test in results["tests_result"]:
tests_result_dict[type_test].append(test[type_test])
return {
"agent_info": results["agent_info"],
"tests_result": tests_result_dict
}
def run_agent(env,
tests_moment,
n_games,
n_episodes,
alpha=0.1,
gamma=0.6,
epsilon=0.1,
n_step=10):
global _ENVIROMENT_CLASS
global _ENV
global _N_GAMES
global _N_EPISODES
global _ALPHA
global _GAMMA
global _EPSILON
global _N_STEP
global _TESTS_MOMENT
_ENVIROMENT_CLASS = enviroment_choose.env_choose(env)
_ENV = env
_N_GAMES = n_games
_N_EPISODES = n_episodes
_ALPHA = alpha
_GAMMA = gamma
_EPSILON = epsilon
_N_STEP = n_step
_TESTS_MOMENT = tests_moment
results = n_step_sarsa()
tests_result_dict = {}
for type_test in _TYPE_TEST_LIST:
tests_result_dict.update({type_test: []})
for type_test in tests_result_dict:
for test in results["tests_result"]:
tests_result_dict[type_test].append(test[type_test])
return {
"agent_info": results["agent_info"],
"tests_result": tests_result_dict
}
for i in range(len(tests_i_agent)):
for j in range(len(tests_i_agent[i][test_type])):
tmp[j][i] = tests_i_agent[i][test_type][j]
percentile_90 = []
for i in range(len(tmp)):
percentile_90.append(np.percentile(tmp[i], 90))
return percentile_90
if __name__ == '__main__':
env_name = input("Insert the enviroment name: ")
env = enviroment_choose.env_choose(env_name)
tests_moment = input("Select the test type (final, on_run, ten_perc): ")
how_group_same_agent = input("Select how group the results of same agent \n" + \
"(Average, 10th percentile, Quartile 1, Median, Quartile 3, 90th percentile): ")
number_of_agent_for_type = int(
input("Insert the number of best agent for every type of agent: "))
base_path = "docs/" + env_name + "/" + tests_moment + "/"
all_agent_tests = {}
all_agent_legend = {}
agent_type_list = [x[1] for x in os.walk(base_path)]
for agent_type in agent_type_list[0]:
path = base_path + agent_type