def get_config():
config = get_base_config()
# required fields.
config.class_path = "liaison.agents.gcn"
config.class_name = "Agent"
config.model = ConfigDict()
config.model.class_path = "liaison.agents.models.gcn_attn_rins"
config.model.n_prop_layers = 4
config.model.node_hidden_layer_sizes = [32]
config.model.edge_hidden_layer_sizes = [32]
config.model.key_dim = 32
config.model.value_dim = 32
config.model.num_heads = 4
config.model.node_embed_dim = 32
config.model.edge_embed_dim = 32
config.query_key_product_hidden_layer_sizes = [16]
config.clip_rho_threshold = 1.0
config.clip_pg_rho_threshold = 1.0
config.loss = ConfigDict()
config.loss.vf_loss_coeff = 1.0
return config
def get_config():
config = get_base_config()
# required fields.
config.class_path = "liaison.agents.ur_discrete"
config.class_name = "Agent"
return config
def get_config():
config = get_base_config()
# 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 = 32
config.model.model_kwargs.node_embed_dim = 32
config.model.model_kwargs.global_embed_dim = 32
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.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 = -1 # disable
config.freeze_graphnet_weights_step = int(1e9)
return config
def get_config(): config = get_base_config() # required fields. config.class_path = "liaison.agents.mlp" config.class_name = "Agent" config.model = ConfigDict() config.model.class_path = "liaison.agents.models.mlp" config.model.hidden_layer_sizes = [32, 32] config.loss = ConfigDict() config.loss.vf_loss_coeff = 1.0 return config
def get_config(): config = get_base_config() # required fields. config.class_path = "liaison.agents.gcn_imitates_mlp" config.class_name = "Agent" config.model = ConfigDict() config.model.class_path = "liaison.agents.models.gcn_rins" config.model.n_prop_layers = 8 config.model.node_hidden_layer_sizes = [32] config.model.edge_hidden_layer_sizes = [32] config.model.sum_aggregation = False config.mlp_model = get_mlp_config().model return config
def get_config():
config = get_base_config()
# required fields.
config.class_path = "liaison.agents.gcn"
config.class_name = "Agent"
# The following code duplicated in gcn_ar.py as well.
# Propagate any changes made
config.model = ConfigDict()
config.model.class_path = "liaison.agents.models.gcn_rins"
config.model.n_prop_layers = 4
config.model.edge_embed_dim = 16
config.model.node_embed_dim = 16
config.model.global_embed_dim = 16
config.model.node_hidden_layer_sizes = [16]
config.model.edge_hidden_layer_sizes = [16]
config.model.policy_torso_hidden_layer_sizes = [16, 16]
config.model.value_torso_hidden_layer_sizes = [16, 16]
config.model.policy_summarize_hidden_layer_sizes = [16]
config.model.value_summarize_hidden_layer_sizes = [16]
config.model.supervised_prediction_torso_hidden_layer_sizes = [16, 16]
config.model.sum_aggregation = False
config.model.use_layer_norm = True
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.choose_stop_switch = False
return config
def get_config():
config = get_base_config()
# required fields.
config.class_path = "liaison.agents.mlp"
config.class_name = "Agent"
config.model = ConfigDict()
config.model.class_path = "liaison.agents.models.transformer_rins"
config.clip_rho_threshold = 1.0
config.clip_pg_rho_threshold = 1.0
config.loss = ConfigDict()
config.loss.vf_loss_coeff = 1.0
return config
def get_config():
config = get_base_config()
# required fields.
config.class_path = "liaison.agents.gcn"
config.class_name = "Agent"
config.model = ConfigDict()
config.model.class_path = "liaison.agents.models.gcn"
config.model.n_prop_layers = 8
config.model.node_embed_dim = 32
config.clip_rho_threshold = 1.0
config.clip_pg_rho_threshold = 1.0
config.loss = ConfigDict()
config.loss.vf_loss_coeff = 1.0
return config