def search(config):
get_params = get_agent_class(config).get_random_config
params_keys = list(get_params().keys())
nb_hp_params = len(params_keys)
if config['debug']:
print('*** Number of hyper-parameters: %d' % nb_hp_params)
config['max_iter'] = 5 if config['debug'] else 500
futures = []
with concurrent.futures.ProcessPoolExecutor(min(multiprocessing.cpu_count(), config['nb_process'])) as executor:
nb_config = 5 if config['debug'] else 200 * nb_hp_params
for i in range(nb_config):
params = get_params(config["fixed_params"])
config.update(params)
config['random_seed'] = 1
futures.append(executor.submit(test_params, i, copy.deepcopy(config), copy.deepcopy(params)))
concurrent.futures.wait(futures)
results = [future.result() for future in futures]
results = sorted(results, key=lambda result: result['mean_score'], reverse=True)
best_params = results[0]['params']
return {
'best_params': best_params
, 'results': results
}
def main(env_id, agent_id, n_test_episodes, render, debug):
rng = np.random.RandomState(42)
if env_id == 'Point-v0':
from environments import point_env
env = gym.make('Point-v0')
else:
env = gym.make('MountainCarContinuous-v0')
env.seed(42)
tf.set_random_seed(42)
np.random.seed(42)
agent_class = agents.get_agent_class(agent_id)
agent = agent_class(env, debug)
agent.train()
episode_rewards = []
for i in range(n_test_episodes):
state = env.reset()
done = False
episode_reward = 0
render_episode = (i % 5 == 0)
while not done:
action = agent.get_action(state)
state, reward, done, _ = env.step(action)
episode_reward += reward
if render and render_episode:
env.render()
episode_rewards.append(episode_reward)
print("Average rewards:", np.mean(episode_rewards))
def third_pass(config, best_lr):
config = copy.deepcopy(config)
config["fixed_params"] = {'lr': best_lr}
config['result_dir_prefix'] = config['result_dir_prefix'] + '/third-pass'
config['games_per_epoch'] = 5 if config['debug'] else 100
dry_run = True if config['debug'] else False
get_params = get_agent_class(config).get_random_config
hb = Hyperband(get_params, run_params)
summary = hb.run(config, skip_last=True, dry_run=dry_run)
return summary
def test_mcagent_act(self):
config = {
'lr': 1 # unused
, 'agent_name': 'TabularMCAgent'
, 'env_name': 'CartPole-v0'
, 'random_seed': 0
, 'result_dir': dir + '/results'
, 'discount': 1.
# 'debug': True
}
np.random.seed(0)
config.update(get_agent_class(config).get_random_config())
config['discount'] = 1.
env = gym.make(config['env_name'])
env.seed(0)
agent = make_agent(config, env)
act, state_id = agent.act(env.reset())
self.assertEqual(act, 1)
self.assertEqual(state_id, 144)
class MagicThing(BasicMagicThing):
off=1
deff=1
opponent_class=get_agent_class()
understand=5
activated=False
activated_description= False
multiple_choice=False
def craft_aid_off(self,opponent):
return self.activated and isinstance(opponent,self.opponent_class) and self.off or 1
def craft_aid_def(self,opponent):
return self.activated and isinstance(opponent,self.opponent_class) and self.deff or 1
def look_at(self,player,gui):
Thing.look_at(self,player,gui)
if not self.activated:
gui.prn("C'è qualcosa misterioso qui.")
understand = self.understand
while understand > 0:
outcome = self.challenge(player,gui)
if not outcome:
player_damage = min((understand+3)//4,player.sp)
gui.prn('Perdi %d punti di spirito.\n' % player_damage)
player.sp -= player_damage
player.long_sp -= player_damage/2
return False
else:
understand = understand-player.craft
self.activated = True
if self.activated_description:
gui.prn(self.activated_description)
return True
def use(self,player,gui):
if self.activated:
BasicMagicThing.use(self,player,gui)
def first_pass(config):
config = copy.deepcopy(config)
config['result_dir_prefix'] = config['result_dir_prefix'] + '/first-pass'
if config['debug']:
print('Removing fixed params')
config["fixed_params"] = {}
config['max_iter'] = 5 if config['debug'] else 150
if config['debug']:
print('Overriding max_iter params to %d' % config['max_iter'])
dry_run = True if config['debug'] else False
get_params = get_agent_class(config).get_random_config
results = []
futures = []
with concurrent.futures.ProcessPoolExecutor(
min(multiprocessing.cpu_count(),
config['nb_process'])) as executor:
nb_config = 5 if config['debug'] else 1000
for i in range(nb_config):
params = get_params(config["fixed_params"])
config.update(params)
futures.append(
executor.submit(exec_first_pass, i, copy.deepcopy(config),
params))
concurrent.futures.wait(futures)
results = []
for future in futures:
results.append(future.result())
return {
'results':
sorted(results, key=lambda result: result['mean_score'], reverse=True)
}
def test_mcagent_learn_from_episode(self):
config = {
'lr': 1 # unused
, 'agent_name': 'TabularMCAgent'
, 'env_name': 'CartPole-v0'
, 'random_seed': 0
, 'result_dir': dir + '/results'
, 'discount': 1.
# 'debug': True
}
np.random.seed(0)
config.update(get_agent_class(config).get_random_config())
config['discount'] = 1.
env = gym.make(config['env_name'])
env.seed(0)
agent = make_agent(config, env)
agent.learn_from_episode(env)
qs = agent.sess.run(agent.Qs)
# for i,q in enumerate(qs):
# print(i,q)
self.assertEqual(np.sum(np.isclose(qs[126], [ 4.49999952, 1.99999976])) == 2, True)
def main(_):
config = flags.FLAGS.__flags.copy()
config["fixed_params"] = json.loads(config["fixed_params"])
# if os.path.isfile(config['result_dir'] + '/config.json'):
# print("Overriding shell configuration with the one found in " + config['result_dir'])
# with open(config['result_dir'] + '/config.json', 'r') as f:
# config = json.loads(f.read())
if config['hyperband']:
print('Starting hyperband search')
config['result_dir_prefix'] = dir + '/results/hyperband/' + str(
int(time.time()))
get_params = get_agent_class(config).get_random_config
hb = Hyperband(get_params, run_params)
results = hb.run(config, skip_last=True, dry_run=config['dry_run'])
if not os.path.exists(config['result_dir_prefix']):
os.makedirs(config['result_dir_prefix'])
with open(config['result_dir_prefix'] + '/hb_results.json', 'w') as f:
json.dump(results, f)
elif config['fullsearch']:
print('*** Starting full search')
config['result_dir_prefix'] = dir + '/results/fullsearch/' + str(
int(time.time())) + '-' + config['agent_name']
os.makedirs(config['result_dir_prefix'])
print('*** Starting first pass: full random search')
summary = fullsearch.first_pass(config)
with open(config['result_dir_prefix'] + '/fullsearch_results1.json',
'w') as f:
json.dump(summary, f)
print('*** Starting second pass: Learning rate search')
best_agent_config = summary['results'][0]['params']
summary = fullsearch.second_pass(config, best_agent_config)
with open(config['result_dir_prefix'] + '/fullsearch_results2.json',
'w') as f:
json.dump(summary, f)
print('*** Starting third pass: Hyperband search with best lr')
best_lr = summary['results'][0]['lr']
summary = fullsearch.third_pass(config, best_lr)
with open(config['result_dir_prefix'] + '/fullsearch_results3.json',
'w') as f:
json.dump(summary, f)
elif config['randomsearch']:
print('*** Starting random search')
config['result_dir_prefix'] = dir + '/results/randomsearch/' + str(
int(time.time())) + '-' + config['agent_name']
os.makedirs(config['result_dir_prefix'])
summary = randomsearch.search(config)
with open(config['result_dir_prefix'] + '/fullsearch_results1.json',
'w') as f:
json.dump(summary, f)
else:
env = gym.make(config['env_name'])
agent = make_agent(config, env)
if config['play']:
for i in range(config['play_nb']):
agent.play(env)
else:
agent.train()
agent.save()
def init_move(self,place):
self.move(place)
if isinstance(place,get_agent_class()):
self.place.place.game_map.all_things.append(self)
else:
self.place.game_map.all_things.append(self)
