def get_env_config():
"""get rins env config."""
config = ConfigDict()
# required fields.
config.class_path = "liaison.env.rins" # should be rel to the parent directory.
config.class_name = "Env"
# makes observations suitable for the MLP model.
config.make_obs_for_mlp = True
# adds all the constraints to MLP state space.
# adds #variables * #constraints dimensions to the state space.
config.mlp_embed_constraints = False
config.make_obs_for_self_attention = False
"""if graph_seed < 0, then use the environment seed"""
config.graph_seed = 42
config.dataset = 'milp-facilities-10'
config.dataset_type = 'train'
config.graph_start_idx = args.graph_start_idx
config.n_graphs = 1
config.k = args.k
config.steps_per_episode = 2000
return config
def get_shell_config():
config = ConfigDict()
agent_config = get_agent_config()
# shell class path is default to the distributed folder.
config.class_path = 'liaison.distributed.shell_for_test'
config.class_name = 'Shell'
config.agent_scope = 'shell'
config.use_gpu = True
config.agent_class = U.import_obj(agent_config.class_name,
agent_config.class_path)
config.agent_config = agent_config
config.agent_config.update(evaluation_mode=True)
return config
def get_config():
config = ConfigDict()
# required fields.
config.class_path = "liaison.env.xor_env" # should be rel to the parent directory.
config.class_name = "Env"
# makes observations suitable for the MLP model.
config.make_obs_for_mlp = False
# makes observations for graphnet agent with node labels in node features and
# shortest path embedded as edge features.
config.make_obs_for_graphnet_semi_supervised = False
"""if graph_seed < 0, then use the environment seed"""
config.graph_seed = 42
return config
def get_config():
config = ConfigDict()
# required fields.
config.class_path = "liaison.env.tsp" # should be rel to the parent directory.
config.class_name = "Env"
# makes observations suitable for the MLP model.
config.make_obs_for_mlp = False
"""if graph_seed < 0, then use the environment seed"""
config.graph_seed = 42
config.dataset = 'tsp-20'
config.dataset_type = 'train'
config.graph_idx = 0
return config
def get_config(): config = ConfigDict() # required fields. config.class_path = "liaison.env.rins_v2" # should be rel to the parent directory. config.class_name = "Env" # makes observations suitable for the MLP model. config.make_obs_for_mlp = False # adds all the constraints to MLP state space. # adds #variables * #constraints dimensions to the state space. config.mlp_embed_constraints = False config.make_obs_for_graphnet = False config.make_obs_for_bipartite_graphnet = True # specify dataset by dataset_path or dataset config.dataset_path = '' config.dataset = '' config.dataset_type = 'train' config.graph_start_idx = 0 config.n_graphs = 100000 config.max_nodes = -1 config.max_edges = -1 config.k = 5 config.n_local_moves = 100 config.lp_features = False config.delta_reward = False config.primal_gap_reward = True config.primal_gap_reward_with_work = False config.work_normalizer = 1. config.disable_maxcuts = False # starting solution hamming distance schedule config.starting_sol_schedule = ConfigDict() config.starting_sol_schedule.enable = False config.starting_sol_schedule.start_value = 1 config.starting_sol_schedule.max_value = 100 config.starting_sol_schedule.start_step = 10000 config.starting_sol_schedule.dec_steps = 25000 # dataset schedule config.dataset_schedule = ConfigDict() config.dataset_schedule.enable = False config.dataset_schedule.datasets = ['milp-cauction-100-filtered', 'milp-cauction-300-filtered'] config.dataset_schedule.start_steps = [50000] # should be len-1 where len is len of datasets. config.k_schedule = ConfigDict() config.k_schedule.enable = False config.k_schedule.values = [5, 10] config.k_schedule.start_steps = [50000] config.n_local_move_schedule = ConfigDict() # if enabled config.n_local_moves will be disabled config.n_local_move_schedule.enable = False config.n_local_move_schedule.start_step = 10000 config.n_local_move_schedule.start_value = 5 config.n_local_move_schedule.max_value = 25 config.n_local_move_schedule.dec_steps = 25000 # add one hot node labels for debugging graphnet models. config.attach_node_labels = False # multi dimensional action space. config.muldi_actions = False config.sample_every_n_resets = 10 config.use_rens_submip_bounds = False config.adapt_k = ConfigDict() config.adapt_k.enable = False config.adapt_k.min_k = 0 # For max_k just use config.k return config
def get_env_config():
config = ConfigDict()
# required fields.
config.class_path = "liaison.env.rins_v2" # should be rel to the parent directory.
config.class_name = "Env"
# makes observations suitable for the MLP model.
config.make_obs_for_mlp = False
# adds all the constraints to MLP state space.
# adds #variables * #constraints dimensions to the state space.
config.mlp_embed_constraints = False
config.make_obs_for_self_attention = False
config.make_obs_for_graphnet = False
config.make_obs_for_bipartite_graphnet = True
# specify dataset by dataset_path or dataset
config.dataset_path = ''
config.dataset = 'milp-cauction-100-filtered'
config.dataset_type = 'train'
config.graph_start_idx = 0
config.n_graphs = 1000
config.max_nodes = 800
config.max_edges = 12000
config.k = 5
config.n_local_moves = 20
config.lp_features = False
config.delta_reward = False
config.primal_gap_reward = True
config.disable_maxcuts = False
# starting solution hamming distance schedule
config.starting_sol_schedule = ConfigDict()
config.starting_sol_schedule.enable = False
config.starting_sol_schedule.start_value = 1
config.starting_sol_schedule.max_value = 100
config.starting_sol_schedule.start_step = 10000
config.starting_sol_schedule.dec_steps = 25000
# dataset schedule
config.dataset_schedule = ConfigDict()
config.dataset_schedule.enable = False
config.dataset_schedule.datasets = [
'milp-cauction-25-filtered', 'milp-cauction-100-filtered', 'milp-cauction-300-filtered'
]
# should be len-1 where len is len of datasets.
config.dataset_schedule.start_steps = [50000, 100000]
# add one hot node labels for debugging graphnet models.
config.attach_node_labels = False
# multi dimensional action space.
config.muldi_actions = True
return config
def get_agent_config():
config = ConfigDict()
config.lr_init = 1e-4
config.lr_min = 1e-7
config.lr_start_dec_step = 1000
config.lr_dec_steps = 1000
config.lr_dec_val = .1
config.lr_dec_approach = 'linear'
config.ent_dec_init = 1e-2
config.ent_dec_min = 0.0
config.ent_dec_steps = 1000
config.ent_start_dec_step = 1000
# dec_val not used for linear scheme
config.ent_dec_val = .1
config.ent_dec_approach = 'linear'
# specify <= 0 here to disable grad clip
config.grad_clip = 1.0
config.discount_factor = 1.0
config.optimizer = ConfigDict()
# Options: Adam or RMSProp.
config.optimizer.name = 'Adam'
# hyperparams for RMSProp
config.optimizer.decay = .9
config.optimizer.momentum = 0.0
config.optimizer.epsilon = 1e-7
# required fields.
config.class_path = "liaison.agents.gcn_multi_actions"
config.class_name = "Agent"
config.model = ConfigDict()
config.model.class_path = 'liaison.agents.models.transformer_auto_regressive'
config.model.num_blocks = 4
config.model.d_ff = 32
config.model.num_heads = 4
config.model.d_model = 64
config.model.dropout_rate = 0.
config.model.use_mlp_value_func = False
# The following code duplicated in gcn_rins.py as well.
# Propagate any changes made as needed.
config.model.model_kwargs = ConfigDict()
config.model.model_kwargs.class_path = "liaison.agents.models.bipartite_gcn_rins"
config.model.model_kwargs.n_prop_layers = 4
config.model.model_kwargs.edge_embed_dim = 16
config.model.model_kwargs.node_embed_dim = 32
config.model.model_kwargs.global_embed_dim = 32
config.model.model_kwargs.node_hidden_layer_sizes = [16, 16]
config.model.model_kwargs.edge_hidden_layer_sizes = [16, 16]
config.model.model_kwargs.policy_torso_hidden_layer_sizes = [16, 16]
config.model.model_kwargs.value_torso_hidden_layer_sizes = [16, 16]
config.model.model_kwargs.policy_summarize_hidden_layer_sizes = [16]
config.model.model_kwargs.value_summarize_hidden_layer_sizes = [16]
config.model.model_kwargs.supervised_prediction_torso_hidden_layer_sizes = [
16, 16
]
config.model.model_kwargs.sum_aggregation = False
config.model.model_kwargs.use_layer_norm = True
config.model.model_kwargs.apply_gradient_to_graphnet_every = 1
config.model.model_kwargs.memory_hack = False
config.clip_rho_threshold = 1.0
config.clip_pg_rho_threshold = 1.0
config.loss = ConfigDict()
config.loss.vf_loss_coeff = 1.0
config.loss.al_coeff = ConfigDict()
config.loss.al_coeff.init_val = 0.
config.loss.al_coeff.min_val = 0.
config.loss.al_coeff.start_decay_step = int(1e10)
config.loss.al_coeff.decay_steps = 5000
# dec_val not used for linear scheme
config.loss.al_coeff.dec_val = .1
config.loss.al_coeff.dec_approach = 'linear'
# applicable for agent 'liaison.agents.gcn_large_batch'
config.apply_grads_every = 1
config.log_features_every = 0
config.freeze_graphnet_weights_step = 50 + 10
return config