def get_predicate_of_evaluated_env(self, evaluated_env: EnvVariables) -> bool: for env_predicate_pair in self.env_predicate_pairs: evaluated_env_variables = env_predicate_pair.get_env_variables() if evaluated_env_variables.is_equal(evaluated_env): return env_predicate_pair.is_predicate() raise AttributeError("{} must be evaluated".format( evaluated_env.get_params_string()))
def is_already_evaluated(self, candidate_env_variables: EnvVariables) -> bool: for env_predicate_pair in self.env_predicate_pairs: evaluated_env_variables = env_predicate_pair.get_env_variables() if evaluated_env_variables.is_equal(candidate_env_variables): self.logger.debug("Env {} was already evaluated".format( candidate_env_variables.get_params_string())) return True return False
def execute_train( agent: AbstractAgent, current_iteration: int, search_suffix: str, current_env_variables: EnvVariables, _start_time: float, random_search: bool = False, ) -> Tuple[EnvPredicatePair, float, float]: env_predicate_pairs = [] communication_queue = Queue() logger = Log("execute_train") # agent.train sets seed globally (for tf, np and random) seed = np.random.randint(2 ** 32 - 1) # order of argument matters in the args param; must match the order of args in the train method of agent thread = threading.Thread( target=agent.train, args=(seed, communication_queue, current_iteration, search_suffix, current_env_variables, random_search,), ) thread.start() sum_training_time = 0.0 sum_regression_time = 0.0 while True: data: ExecutionResult = communication_queue.get() # blocking code logger.debug( "Env: {}, evaluates to {}".format(current_env_variables.get_params_string(), data.is_adequate_performance(),) ) logger.debug("Info: {}".format(data.get_info())) env_predicate_pairs.append( EnvPredicatePair( env_variables=current_env_variables, predicate=data.is_adequate_performance(), regression=data.is_regression(), execution_info=data.get_info(), model_dirs=[search_suffix], ) ) sum_regression_time += data.get_regression_time() sum_training_time += data.get_training_time() if data.is_task_completed(): break while thread.is_alive(): time.sleep(1.0) logger.info("TIME ELAPSED: {}".format(str(datetime.timedelta(seconds=(time.time() - _start_time))))) return env_predicate_pairs[-1], sum_training_time, sum_regression_time
def append(self, t_env_variables: EnvVariables, f_env_variables: EnvVariables) -> bool: assert is_frontier_pair( t_env_variables=t_env_variables, f_env_variables=f_env_variables, epsilon=self.epsilon ), "The pair t_env: {} - f_env: {} is not a frontier pair since its distance {} is > {}".format( t_env_variables.get_params_string(), f_env_variables.get_params_string(), compute_dist(t_env_variables=t_env_variables, f_env_variables=f_env_variables), self.epsilon, ) candidate_frontier_pair = FrontierPair(t_env_variables, f_env_variables) for frontier_pair in self.frontier_pairs: if frontier_pair.is_equal(candidate_frontier_pair): return False self.logger.info( "New frontier pair found. t_env: {}, f_env: {}".format( t_env_variables.get_params_string(), f_env_variables.get_params_string())) self.frontier_pairs.append(candidate_frontier_pair) return True
def test_with_callback(self, seed, env_variables: EnvVariables, n_eval_episodes: int = None) -> EnvPredicatePair: assert self.env_eval_callback, "env_eval_callback should be instantiated" self._set_global_seed(seed=seed) self.logger.debug("env_variables: {}".format(env_variables.get_params_string())) best_model_save_path, tensorboard_log_dir = self._preprocess_storage_dirs() if self.algo_hyperparams: self.logger.debug("Overriding file specified hyperparams with {}".format(eval(self.algo_hyperparams))) hyperparams = eval(self.algo_hyperparams) else: hyperparams = load_hyperparams(algo_name=self.algo_name, env_name=self.env_name) normalize_kwargs = _parse_normalize(dictionary=hyperparams) eval_env = make_custom_env( seed=seed, sb_version=self.sb_version, env_kwargs=env_variables, algo_name=self.algo_name, env_name=self.env_name, normalize_kwargs=normalize_kwargs, log_dir=best_model_save_path, evaluate=True, continue_learning_suffix=self.continue_learning_suffix, ) model = self.create_model( seed=seed, algo_name=self.algo_name, env=eval_env, tensorboard_log_dir=tensorboard_log_dir, hyperparams=hyperparams, best_model_save_path=best_model_save_path, model_to_load=self.model_to_load, env_name=self.env_name, ) n_eval_episodes_to_run = n_eval_episodes if n_eval_episodes else self.n_eval_episodes adequate_performance, info = self.env_eval_callback.evaluate_env( model=model, env=eval_env, n_eval_episodes=n_eval_episodes_to_run, sb_version=self.sb_version, ) return EnvPredicatePair(env_variables=env_variables, predicate=adequate_performance, execution_info=info,)
def get_binary_search_candidate( t_env_variables: EnvVariables, f_env_variables: EnvVariables, algo_name: str, env_name: str, param_names, discrete_action_space: bool, buffer_env_predicate_pairs: BufferEnvPredicatePairs, ) -> EnvVariables: original_max_iterations = 50 logger = Log("get_binary_search_candidate") max_number_iterations = original_max_iterations candidate_new_env_variables = copy.deepcopy(t_env_variables) while True: # compute all possible combinations of environments candidates_dict = dict() t_f_env_variables = random.choice([(t_env_variables, True), (f_env_variables, False)]) for i in range(len(t_env_variables.get_params())): new_value = ( t_env_variables.get_param(index=i).get_current_value() + f_env_variables.get_param(index=i).get_current_value()) / 2 if i not in candidates_dict: candidates_dict[i] = [] if (t_env_variables.get_param(index=i).get_current_value() != f_env_variables.get_param(index=i).get_current_value()): candidates_dict[i].append(new_value) for index in range(len(t_env_variables.get_params())): if index not in candidates_dict: candidates_dict[index] = [] if index != i: candidates_dict[index].append( t_f_env_variables[0].get_values()[index]) all_candidates = list( itertools.product(*list(candidates_dict.values()))) logger.info("t_env: {}, f_env: {}".format( t_env_variables.get_params_string(), f_env_variables.get_params_string())) logger.info("all candidates binary search: {}".format(all_candidates)) all_candidates_env_variables_filtered = [] all_candidates_env_variables = [] for candidate_values in all_candidates: env_values = dict() for i in range(len(t_f_env_variables[0].get_params())): param_name = t_f_env_variables[0].get_param(index=i).get_name() env_values[param_name] = candidate_values[i] candidate_env_variables = instantiate_env_variables( algo_name=algo_name, discrete_action_space=discrete_action_space, env_name=env_name, param_names=param_names, env_values=env_values, ) # do not consider candidate = t_f_env_variables if not candidate_env_variables.is_equal( t_env_variables) and not candidate_env_variables.is_equal( f_env_variables): if not buffer_env_predicate_pairs.is_already_evaluated( candidate_env_variables=candidate_env_variables): all_candidates_env_variables_filtered.append( candidate_env_variables) all_candidates_env_variables.append(candidate_env_variables) if len(all_candidates_env_variables_filtered) > 0: candidate_new_env_variables = random.choice( all_candidates_env_variables_filtered) break else: assert len( all_candidates ) > 0, "there must be at least one candidate env for binary search" candidate_env_variables_already_evaluated = random.choice( all_candidates_env_variables_filtered) if t_f_env_variables[1]: t_env_variables = copy.deepcopy( candidate_env_variables_already_evaluated) else: f_env_variables = copy.deepcopy( candidate_env_variables_already_evaluated) max_number_iterations -= 1 if max_number_iterations == 0: break assert max_number_iterations > 0, "Could not binary mutate any param of envs {} and {} in {} steps".format( t_env_variables.get_params_string(), f_env_variables.get_params_string(), str(original_max_iterations)) assert not candidate_new_env_variables.is_equal( t_env_variables ) and not candidate_new_env_variables.is_equal( f_env_variables ), "candidate_env_variables {} must be different than t_env_variables {} and f_env_variables {}".format( candidate_new_env_variables.get_params_string(), t_env_variables.get_params_string(), f_env_variables.get_params_string(), ) return candidate_new_env_variables
def dominance_analysis( self, candidate_env_variables: EnvVariables, predicate_to_consider: bool = True ) -> Union[EnvPredicatePair, None]: assert not self.is_already_evaluated( candidate_env_variables=candidate_env_variables ), "Env {} must not be evaluated".format( candidate_env_variables.get_params_string()) executed_env_dominate = None if predicate_to_consider: # searching for an executed env that evaluates to True that dominates the env passed as parameter for env_predicate_pair in self.env_predicate_pairs: predicate = env_predicate_pair.is_predicate() if predicate: dominates = True for i in range( len(env_predicate_pair.get_env_variables(). get_params())): direction = env_predicate_pair.get_env_variables( ).get_param(index=i).get_direction() starting_multiplier = ( env_predicate_pair.get_env_variables().get_param( index=i).get_starting_multiplier()) assert direction == "positive", "unknown and negative direction is not supported" env_value = env_predicate_pair.get_env_variables( ).get_param(index=i).get_current_value() other_env_value = candidate_env_variables.get_param( index=i).get_current_value() if direction == "positive" and starting_multiplier > 1.0: if env_value < other_env_value: dominates = False elif direction == "positive" and starting_multiplier < 1.0: if env_value > other_env_value: dominates = False if dominates: executed_env_dominate = env_predicate_pair self.logger.debug( "candidate {} dominated by executed env {} that evaluates to {}" .format( candidate_env_variables.get_params_string(), env_predicate_pair.get_env_variables(). get_params_string(), predicate, )) else: # searching for an executed env that evaluates to False that is dominated by the env passed as parameter for env_predicate_pair in self.env_predicate_pairs: predicate = env_predicate_pair.is_predicate() if not predicate: is_dominated = True for i in range( len(env_predicate_pair.get_env_variables(). get_params())): direction = env_predicate_pair.get_env_variables( ).get_param(index=i).get_direction() starting_multiplier = ( env_predicate_pair.get_env_variables().get_param( index=i).get_starting_multiplier()) assert direction == "positive", "unknown and negative direction is not supported" env_value = env_predicate_pair.get_env_variables( ).get_param(index=i).get_current_value() other_env_value = candidate_env_variables.get_param( index=i).get_current_value() if direction == "positive" and starting_multiplier > 1.0: if other_env_value < env_value: is_dominated = False elif direction == "positive" and starting_multiplier < 1.0: if other_env_value > env_value: is_dominated = False if is_dominated: executed_env_dominate = env_predicate_pair self.logger.debug( "candidate {} dominates executed env {} that evaluates to {}" .format( candidate_env_variables.get_params_string(), env_predicate_pair.get_env_variables(). get_params_string(), not predicate, )) return executed_env_dominate