routing_modes = [
"Q_routing_2_hop", "Q_routing_1_hop", "Q_routing_0_hop", "Q_routing",
"TTSPWRR", "TTSP"
]
network_names = ["5x6", "UES_Manhatan", "toronto"]
gpu_num = int(sys.argv[1])
algorithm_num = int(sys.argv[2])
network_num = int(sys.argv[3])
config = Config()
config.use_GPU = True
assert (torch.cuda.is_available())
config.device = torch.device(gpu_num)
config.routing_mode = routing_modes[algorithm_num]
network_name = network_names[network_num]
config.training_mode = True
config.does_need_network_state = config.routing_mode in [
"Q_routing_2_hop", "Q_routing_1_hop", "Q_routing_0_hop"
]
config.does_need_network_state_embeding = config.routing_mode in [
"Q_routing_2_hop", "Q_routing_1_hop"
]
config.retain_graph = config.does_need_network_state_embeding
# config.exp_name=config.routing_mode
config.should_load_model= False if config.routing_mode== "TTSPWRR" or \
config = Config()
config.seed = 1
config.num_episodes_to_run = 8000
# config.file_to_save_data_results = "results/data_and_graphs/VEC.pkl"
# config.file_to_save_results_graph = "results/data_and_graphs/VEC.png"
config.show_solution_score = False
config.visualise_individual_results = False
config.visualise_overall_agent_results = False
config.standard_deviation_results = 1.0
config.runs_per_agent = 1
config.use_GPU = True
config.overwrite_existing_results_file = False
config.randomise_random_seed = True
config.save_model = False
config.device = "cuda:0"
config.hyperparameters = {
"DQN_Agents": {
"learning_rate": 0.00002,
"batch_size": 256,
"buffer_size": 100000,
"epsilon_decay_rate_denominator": 150,
"discount_rate": 0.99,
"incremental_td_error": 1e-8,
"update_every_n_steps": 1,
"linear_hidden_units": [1000,800],
"final_layer_activation": None,
"batch_norm": False,
"gradient_clipping_norm": 5,
"HER_sample_proportion": 0.8,
