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)