def _get_algorithm_kwargs(self, variant): algorithm_kwargs = super()._get_algorithm_kwargs(variant) algorithm_type = variant['algorithm_params']['type'] # TODO: Replace this with a common API for single vs multigoal # === SINGLE GOAL POOL === if algorithm_type in ('SACClassifier', 'RAQ', 'VICE', 'VICEGAN', 'VICERAQ', 'VICEDynamicsAware', 'DynamicsAwareEmbeddingVICE'): reward_classifier = self.reward_classifier = ( get_reward_classifier_from_variant( self._variant, algorithm_kwargs['training_environment'])) algorithm_kwargs['classifier'] = reward_classifier goal_examples_train, goal_examples_validation = ( get_goal_example_from_variant(variant)) algorithm_kwargs['goal_examples'] = goal_examples_train algorithm_kwargs['goal_examples_validation'] = ( goal_examples_validation) if algorithm_type == 'VICEDynamicsAware': algorithm_kwargs['dynamics_model'] = ( get_dynamics_model_from_variant( self._variant, algorithm_kwargs['training_environment'])) elif algorithm_type == 'DynamicsAwareEmbeddingVICE': # TODO(justinvyu): Get this working for any environment self.distance_fn = algorithm_kwargs['distance_fn'] = ( reward_classifier. observations_preprocessors['state_observation']) # TODO(justinvyu): include goal state as one of the VICE goal exmaples? algorithm_kwargs['goal_state'] = None # === LOAD GOAL POOLS FOR MULTI GOAL === elif algorithm_type in ('VICEGANMultiGoal', 'MultiVICEGAN'): goal_pools_train, goal_pools_validation = ( get_example_pools_from_variant(variant)) num_goals = len(goal_pools_train) reward_classifiers = self.reward_classifiers = tuple( get_reward_classifier_from_variant( variant, algorithm_kwargs['training_environment']) for _ in range(num_goals)) algorithm_kwargs['classifiers'] = reward_classifiers algorithm_kwargs['goal_example_pools'] = goal_pools_train algorithm_kwargs[ 'goal_example_validation_pools'] = goal_pools_validation elif algorithm_type == 'SQIL': goal_transitions = get_goal_transitions_from_variant(variant) algorithm_kwargs['goal_transitions'] = goal_transitions return algorithm_kwargs
def _build(self): variant = copy.deepcopy(self._variant) training_environment = self.training_environment = ( get_goal_example_environment_from_variant(variant)) evaluation_environment = self.evaluation_environment = ( get_goal_example_environment_from_variant(variant)) replay_pool = self.replay_pool = (get_replay_pool_from_variant( variant, training_environment)) sampler = self.sampler = get_sampler_from_variant(variant) # 创建网络 Dense :inputs:[state,action] outputs:size=1 Qs = self.Qs = get_Q_function_from_variant(variant, training_environment) policy = self.policy = get_policy_from_variant(variant, training_environment, Qs) initial_exploration_policy = self.initial_exploration_policy = ( get_policy('UniformPolicy', training_environment)) algorithm_kwargs = { 'variant': self._variant, 'training_environment': self.training_environment, 'evaluation_environment': self.evaluation_environment, 'policy': policy, 'initial_exploration_policy': initial_exploration_policy, 'Qs': Qs, 'pool': replay_pool, 'sampler': sampler, 'session': self._session, } if self._variant['algorithm_params']['type'] in [ 'SACClassifier', 'RAQ', 'VICE', 'VICEGAN', 'VICERAQ' ]: reward_classifier = self.reward_classifier \ = get_reward_classifier_from_variant(self._variant, training_environment) algorithm_kwargs['classifier'] = reward_classifier goal_examples_train, goal_examples_validation = \ get_goal_example_from_variant(variant) algorithm_kwargs['goal_examples'] = goal_examples_train algorithm_kwargs['goal_examples_validation'] = \ goal_examples_validation self.algorithm = get_algorithm_from_variant(**algorithm_kwargs) initialize_tf_variables(self._session, only_uninitialized=True) self._built = True
def _build(self): variant = copy.deepcopy(self._variant) print(variant.keys()) env = self.env = get_environment_from_params( variant['environment_params']['training']) replay_pool = self.replay_pool = (get_replay_pool_from_variant( variant, env)) sampler = self.sampler = get_sampler_from_variant(variant) Qs = self.Qs = get_Q_function_from_variant(variant, env) policy = self.policy = get_policy_from_variant(variant, env, Qs) initial_exploration_policy = self.initial_exploration_policy = ( get_policy('UniformPolicy', env)) algorithm_kwargs = { 'variant': self._variant, 'env': self.env, 'policy': policy, 'initial_exploration_policy': initial_exploration_policy, 'Qs': Qs, 'pool': replay_pool, 'sampler': sampler, 'session': self._session, } if self._variant['algorithm_params']['type'] in CLASSIFIER_RL_ALGS: reward_classifier = self.reward_classifier \ = get_reward_classifier_from_variant(self._variant, env) algorithm_kwargs['classifier'] = reward_classifier goal_examples_train, goal_examples_validation = \ get_goal_example_from_variant(variant) algorithm_kwargs['goal_examples'] = goal_examples_train algorithm_kwargs['goal_examples_validation'] = \ goal_examples_validation self.algorithm = get_algorithm_from_variant(**algorithm_kwargs) initialize_tf_variables(self._session, only_uninitialized=True) self._built = True
def _build(self): variant = copy.deepcopy(self._variant) #training_environment = self.training_environment = ( # get_goal_example_environment_from_variant( # variant['task'], gym_adapter=False)) training_environment = self.training_environment = (GymAdapter( domain=variant['domain'], task=variant['task'], **variant['env_params'])) #evaluation_environment = self.evaluation_environment = ( # get_goal_example_environment_from_variant( # variant['task_evaluation'], gym_adapter=False)) evaluation_environment = self.evaluation_environment = (GymAdapter( domain=variant['domain'], task=variant['task_evaluation'], **variant['env_params'])) # training_environment = self.training_environment = ( # flatten_multiworld_env(self.training_environment)) # evaluation_environment = self.evaluation_environment = ( # flatten_multiworld_env(self.evaluation_environment)) #training_environment = self.training_environment = ( # GymAdapter(env=training_environment)) #evaluation_environment = self.evaluation_environment = ( # GymAdapter(env=evaluation_environment)) # make sure this is her replay pool replay_pool = self.replay_pool = (get_replay_pool_from_variant( variant, training_environment)) sampler = self.sampler = get_sampler_from_variant(variant) Qs = self.Qs = get_Q_function_from_variant(variant, training_environment) policy = self.policy = get_policy_from_variant(variant, training_environment) initial_exploration_policy = self.initial_exploration_policy = ( get_policy_from_params(variant['exploration_policy_params'], training_environment)) algorithm_kwargs = { 'variant': self._variant, 'training_environment': self.training_environment, 'evaluation_environment': self.evaluation_environment, 'policy': policy, 'initial_exploration_policy': initial_exploration_policy, 'Qs': Qs, 'pool': replay_pool, 'sampler': sampler, 'session': self._session, } if self._variant['algorithm_params']['type'] in [ 'VICEGoalConditioned', 'VICEGANGoalConditioned' ]: reward_classifier = self.reward_classifier = ( get_reward_classifier_from_variant(self._variant, training_environment)) algorithm_kwargs['classifier'] = reward_classifier # goal_examples_train, goal_examples_validation = \ # get_goal_example_from_variant(variant) algorithm_kwargs['goal_examples'] = np.empty((1, 1)) algorithm_kwargs['goal_examples_validation'] = np.empty((1, 1)) # RND if variant['algorithm_params']['rnd_params']: from softlearning.rnd.utils import get_rnd_networks_from_variant rnd_networks = get_rnd_networks_from_variant( variant, training_environment) else: rnd_networks = () algorithm_kwargs['rnd_networks'] = rnd_networks self.algorithm = get_algorithm_from_variant(**algorithm_kwargs) initialize_tf_variables(self._session, only_uninitialized=True) self._built = True