def __init__(self, use_dataset: bool = True): super().__init__("Task 0 - Base topology", inputs=Task0BaseGroupInputs(self), outputs=Task0BaseGroupOutputs(self)) if use_dataset: params = DatasetSeObjectsParams( dataset_config=DatasetConfig.TRAIN_TEST, save_gpu_memory=True) self.se_node = DatasetSeObjectsNode(params) else: se_config = SpaceEngineersConnectorConfig() se_config.curriculum = list((0, -1)) actions_descriptor = SpaceEngineersActionsDescriptor() self.se_node = SpaceEngineersConnectorNode(actions_descriptor, se_config)
def __init__(self, curriculum: tuple = (1, -1)): super().__init__() se_config = SpaceEngineersConnectorConfig() se_config.render_width = 16 se_config.render_height = 16 se_config.curriculum = list(curriculum) base_expert_params = ExpertParams() base_expert_params.flock_size = 1 base_expert_params.n_cluster_centers = 100 base_expert_params.compute_reconstruction = False base_expert_params.spatial.cluster_boost_threshold = 1000 base_expert_params.spatial.learning_rate = 0.2 base_expert_params.spatial.batch_size = 1000 base_expert_params.spatial.buffer_size = 1010 base_expert_params.spatial.learning_period = 100 base_expert_params.temporal.batch_size = 1000 base_expert_params.temporal.buffer_size = 1010 base_expert_params.temporal.learning_period = 200 base_expert_params.temporal.forgetting_limit = 20000 # parent_expert_params = ExpertParams() # parent_expert_params.flock_size = 1 # parent_expert_params.n_cluster_centers = 20 # parent_expert_params.compute_reconstruction = True # parent_expert_params.temporal.exploration_probability = 0.9 # parent_expert_params.spatial.cluster_boost_threshold = 1000 # parent_expert_params.spatial.learning_rate = 0.2 # parent_expert_params.spatial.batch_size = 1000 # parent_expert_params.spatial.buffer_size = 1010 # parent_expert_params.spatial.learning_period = 100 # parent_expert_params.temporal.context_without_rewards_size = se_config.LOCATION_SIZE_ONE_HOT # SE nodes actions_descriptor = SpaceEngineersActionsDescriptor() node_se_connector = SpaceEngineersConnectorNode( actions_descriptor, se_config) node_action_monitor = ActionMonitorNode(actions_descriptor) # flock-related nodes flock_node = ExpertFlockNode(base_expert_params) blank_task_control = ConstantNode((se_config.TASK_CONTROL_SIZE, )) blank_task_labels = ConstantNode((20, )) # parent_flock_node = ExpertFlockNode(parent_expert_params) join_node = JoinNode(flatten=True) actions = ['FORWARD', 'BACKWARD', 'LEFT', 'RIGHT'] action_count = len(actions) pass_actions_node = PassNode(output_shape=(action_count, ), name="pass actions") fork_node = ForkNode( 0, [base_expert_params.n_cluster_centers, action_count]) def squeeze(inputs, outputs): outputs[0].copy_(inputs[0].squeeze()) squeeze_node = LambdaNode( squeeze, 1, [(base_expert_params.n_cluster_centers + action_count, )], name="squeeze lambda node") def stack_and_unsqueeze(inputs, outputs): outputs[0].copy_(torch.stack([inputs[0], inputs[1]]).unsqueeze(0)) stack_unsqueeze_node = LambdaNode( stack_and_unsqueeze, 2, [(1, 2, se_config.LOCATION_SIZE_ONE_HOT)], name="stack and unsqueeze node") to_one_hot_node = ToOneHotNode() action_parser_node = AgentActionsParserNode(actions_descriptor, actions) random_node = RandomNumberNode(0, action_count, name="random action generator", generate_new_every_n=5, randomize_intervals=True) switch_node = SwitchNode(2) # add nodes to the graph self.add_node(flock_node) # self.add_node(parent_flock_node) self.add_node(node_se_connector) self.add_node(node_action_monitor) self.add_node(blank_task_control) self.add_node(blank_task_labels) # self.add_node(join_node) # self.add_node(fork_node) # self.add_node(pass_actions_node) # self.add_node(squeeze_node) # self.add_node(to_one_hot_node) # self.add_node(stack_unsqueeze_node) self.add_node(action_parser_node) self.add_node(random_node) # self.add_node(switch_node) # first layer Connector.connect(node_se_connector.outputs.image_output, flock_node.inputs.sp.data_input) # Connector.connect( # flock_node.outputs.tp.projection_outputs, # join_node.inputs[0] # ) # Connector.connect( # pass_actions_node.outputs.output, # join_node.inputs[1] # ) # # second layer # Connector.connect( # join_node.outputs.output, # parent_flock_node.inputs.sp.data_input # ) # Connector.connect( # node_se_connector.outputs.task_to_agent_location_one_hot, # stack_unsqueeze_node.inputs[0] # ) # Connector.connect( # node_se_connector.outputs.task_to_agent_location_target_one_hot, # stack_unsqueeze_node.inputs[1] # ) # Connector.connect( # stack_unsqueeze_node.outputs[0], # parent_flock_node.inputs.tp.context_input # ) # # # actions # Connector.connect( # parent_flock_node.outputs.sp.predicted_reconstructed_input, # squeeze_node.inputs[0] # ) # Connector.connect( # squeeze_node.outputs[0], # fork_node.inputs.input # ) # Connector.connect( # fork_node.outputs[1], # to_one_hot_node.inputs.input # ) # Connector.connect( # random_node.outputs.one_hot_output, # switch_node.inputs[0] # ) # Connector.connect( # to_one_hot_node.outputs.output, # switch_node.inputs[1] # ) # Connector.connect( # switch_node.outputs.output, # action_parser_node.inputs.input # ) # directly use random exploration Connector.connect(random_node.outputs.one_hot_output, action_parser_node.inputs.input) Connector.connect(action_parser_node.outputs.output, node_action_monitor.inputs.action_in) # Connector.connect( # switch_node.outputs.output, # pass_actions_node.inputs.input, # is_low_priority=True # ) Connector.connect( node_action_monitor.outputs.action_out, node_se_connector.inputs.agent_action, # is_low_priority=True is_backward=False) # blank connection Connector.connect(blank_task_control.outputs.output, node_se_connector.inputs.task_control) Connector.connect(blank_task_labels.outputs.output, node_se_connector.inputs.agent_to_task_label) # Save the SE connector so we can check testing/training phase. # When the se_io interface has been added, this can be removed. self._node_se_connector = node_se_connector
def __init__(self, curriculum: tuple = (1, -1)): super().__init__("Task 1 - Base topology world", inputs=Task1BaseGroupWorldInputs(self), outputs=Task1BaseGroupWorldOutputs(self)) actions_descriptor = SpaceEngineersActionsDescriptor() actions = ['FORWARD', 'BACKWARD', 'LEFT', 'RIGHT'] action_count = len(actions) # SE nodes se_config = SpaceEngineersConnectorConfig() se_config.curriculum = list(curriculum) node_se_connector = SpaceEngineersConnectorNode(actions_descriptor, se_config) def node_se_connector_is_learning() -> bool: if node_se_connector.outputs.metadata_testing_phase.tensor is None: return False else: return node_se_connector.outputs.metadata_testing_phase.tensor.cpu().item() == 1 node_se_connector.is_learning = node_se_connector_is_learning self.node_se_connector = node_se_connector blank_task_control = ConstantNode((se_config.TASK_CONTROL_SIZE,)) pass_actions_node = PassNode(output_shape=(action_count,), name="pass actions") blank_task_labels = ConstantNode((20,)) self.add_node(node_se_connector) self.add_node(blank_task_control) self.add_node(pass_actions_node) self.add_node(blank_task_labels) Connector.connect( self.inputs.last_actions.output, pass_actions_node.inputs.input ) Connector.connect( self.inputs.current_actions.output, node_se_connector.inputs.agent_action ) Connector.connect( pass_actions_node.outputs.output, self.outputs.actions.input ) Connector.connect( node_se_connector.outputs.image_output, self.outputs.image.input ) Connector.connect( node_se_connector.outputs.task_to_agent_location_one_hot, self.outputs.current_location.input ) Connector.connect( node_se_connector.outputs.task_to_agent_location_target_one_hot, self.outputs.target_location.input ) # blank connection Connector.connect(blank_task_control.outputs.output, node_se_connector.inputs.task_control) Connector.connect(blank_task_labels.outputs.output, node_se_connector.inputs.agent_to_task_label)