def get_options(options: Dict):
f = lambda k, t: None if options[k] == 'None' else t(options[k])
op = Config()
op.add_dict(
dict([['inference', bool(options['--inference'])],
['algo', str(options['--algorithm'])],
['algo_config', f('--config-file', str)],
['env', f('--env', str)], ['port',
int(options['--port'])],
['unity', bool(options['--unity'])],
['graphic', bool(options['--graphic'])],
['name', f('--name', str)],
['save_frequency', f('--save-frequency', int)],
['models', int(options['--models'])],
['store_dir', f('--store-dir', str)],
['seed', int(options['--seed'])],
['max_step', f('--max-step', int)],
['max_episode', f('--max-episode', int)],
['sampler', f('--sampler', str)], ['load',
f('--load', str)],
['fill_in', bool(options['--fill-in'])],
['prefill_choose',
bool(options['--prefill-choose'])],
['gym', bool(options['--gym'])],
['gym_agents', int(options['--gym-agents'])],
['gym_env', str(options['--gym-env'])],
['gym_env_seed', int(options['--gym-env-seed'])],
['render_episode', f('--render-episode', int)],
['info', f('--info', str)]]))
return op
def run():
if sys.platform.startswith('win'):
import win32api
import win32con
import _thread
def _win_handler(event, hook_sigint=_thread.interrupt_main):
if event == 0:
hook_sigint()
return 1
return 0
# Add the _win_handler function to the windows console's handler function list
win32api.SetConsoleCtrlHandler(_win_handler, 1)
options = docopt(__doc__)
options = get_options(dict(options))
print(options)
default_config = load_yaml(f'config.yaml')
# gym > unity > unity_env
model_args = Config(**default_config['model'])
train_args = Config(**default_config['train'])
env_args = Config()
buffer_args = Config(**default_config['buffer'])
model_args.algo = options.algo
model_args.use_rnn = options.use_rnn
model_args.algo_config = options.algo_config
model_args.seed = options.seed
model_args.load = options.load
env_args.env_num = options.n_copys
if options.gym:
train_args.add_dict(default_config['gym']['train'])
train_args.update({'render_episode': options.render_episode})
env_args.add_dict(default_config['gym']['env'])
env_args.type = 'gym'
env_args.env_name = options.gym_env
env_args.env_seed = options.gym_env_seed
else:
train_args.add_dict(default_config['unity']['train'])
env_args.add_dict(default_config['unity']['env'])
env_args.type = 'unity'
env_args.port = options.port
env_args.sampler_path = options.sampler
env_args.env_seed = options.unity_env_seed
if options.unity:
env_args.file_path = None
env_args.env_name = 'unity'
else:
env_args.update({'file_path': options.env})
if os.path.exists(env_args.file_path):
env_args.env_name = options.unity_env or os.path.join(
*os.path.split(env_args.file_path)[0].replace('\\', '/').replace(r'//', r'/').split('/')[-2:]
)
if 'visual' in env_args.env_name.lower():
# if traing with visual input but do not render the environment, all 0 obs will be passed.
options.graphic = True
else:
raise Exception('can not find this file.')
if options.inference:
env_args.train_mode = False
env_args.render = True
else:
env_args.train_mode = True
env_args.render = options.graphic
train_args.index = 0
train_args.name = NAME
train_args.use_wandb = options.use_wandb
train_args.inference = options.inference
train_args.prefill_choose = options.prefill_choose
train_args.base_dir = os.path.join(options.store_dir or BASE_DIR, env_args.env_name, model_args.algo)
train_args.update(
dict([
['name', options.name],
['max_step_per_episode', options.max_step_per_episode],
['max_train_step', options.max_train_step],
['max_train_frame', options.max_train_frame],
['max_train_episode', options.max_train_episode],
['save_frequency', options.save_frequency],
['pre_fill_steps', options.prefill_steps],
['info', options.info]
])
)
if options.inference:
Agent(env_args, model_args, buffer_args, train_args).evaluate()
return
trails = options.models
if trails == 1:
agent_run(env_args, model_args, buffer_args, train_args)
elif trails > 1:
processes = []
for i in range(trails):
_env_args = deepcopy(env_args)
_model_args = deepcopy(model_args)
_model_args.seed += i * 10
_buffer_args = deepcopy(buffer_args)
_train_args = deepcopy(train_args)
_train_args.index = i
if _env_args.type == 'unity':
_env_args.port = env_args.port + i
p = Process(target=agent_run, args=(_env_args, _model_args, _buffer_args, _train_args))
p.start()
time.sleep(10)
processes.append(p)
[p.join() for p in processes]
else:
raise Exception('trials must be greater than 0.')
