'use_attention': use_attention
})
if use_attention:
if use_hypernet:
gnn_conf = MultiStepInputNetworkConfig(
hist_rnn_conf={
'input_size': node_input_dim,
'hidden_size': rnn_hidden_size
},
hist_enc_conf={
'num_layers': enc_gnn_num_layer,
'model_dim': node_input_dim,
'use_hypernet': use_hypernet,
'hypernet_input_dim': num_relations,
'num_relations': None,
'num_neurons': num_neurons,
'num_head': num_attn_head
},
curr_enc_conf={
'num_layers': enc_gnn_num_layer,
'model_dim': node_input_dim,
'use_hypernet': use_hypernet,
'hypernet_input_dim': num_relations,
'num_relations': None,
'num_neurons': num_neurons,
'num_head': num_attn_head
})
else:
gnn_conf = MultiStepInputNetworkConfig(
hist_rnn_conf={
'input_size': node_input_dim,
reward_name = 'victory_if_zero_enemy'
qnet_conf = HierarchicalMultiStepInputQnetConfig(
multi_step_input_qnet_conf={'exploration_method': 'clustered_random'},
qnet_actor_conf={
'spectral_norm': spectral_norm,
'node_input_dim': node_input_dim,
'pooling_op': pooling_op,
'use_concat_input': use_concat_input,
'init_node_dim': node_input_dim,
'pooling_init': pooling_init,
'num_neurons': num_neurons
},
mixer_conf={'rectifier': mixer_rectifier})
if use_attention:
gnn_conf = MultiStepInputNetworkConfig()
else:
gnn_conf = MultiStepInputGraphNetworkConfig(
hist_rnn_conf={'input_size': node_input_dim},
hist_enc_conf={
'spectral_norm': spectral_norm,
'model_dim': node_input_dim,
'use_concat': use_concat_input_gnn,
'num_neurons': num_neurons
},
curr_enc_conf={
'spectral_norm': spectral_norm,
'model_dim': node_input_dim,
'use_concat': use_concat_input_gnn,
'num_neurons': num_neurons
})