├── Experimental Dataset ├── node10_14_21_topo │ ├── topology_node_10.xml │ ├── topology_node_14.xml │ └── topology_node_21.xml ├── node10_data │ ├── 10-2023-11-20-15-43-12.pkl │ ├── 100-2023-11-20-15-58-13.pkl │ ├── 101-2023-11-20-15-58-23.pkl │ ├── 103-2023-11-20-15-58-43.pkl │ ├── 106-2023-11-20-15-59-13.pkl │ ├── 107-2023-11-20-15-59-23.pkl │ ├── 109-2023-11-20-15-59-43.pkl │ ├── 111-2023-11-20-16-00-03.pkl │ ├── 112-2023-11-20-16-00-13.pkl │ ├── 113-2023-11-20-16-00-23.pkl │ ├── 115-2023-11-20-16-00-43.pkl │ ├── 116-2023-11-20-16-00-53.pkl │ ├── 117-2023-11-20-16-01-03.pkl │ ├── 119-2023-11-20-16-01-23.pkl │ ├── 12-2023-11-20-15-43-32.pkl │ ├── 121-2023-11-20-16-01-43.pkl │ ├── 123-2023-11-20-16-02-03.pkl │ ├── 124-2023-11-20-16-02-13.pkl │ ├── 126-2023-11-20-16-02-33.pkl │ ├── 127-2023-11-20-16-02-43.pkl │ ├── 129-2023-11-20-16-03-03.pkl │ ├── 130-2023-11-20-16-03-13.pkl │ ├── 132-2023-11-20-16-03-33.pkl │ ├── 133-2023-11-20-16-03-43.pkl │ ├── 136-2023-11-20-16-04-14.pkl │ ├── 138-2023-11-20-16-04-34.pkl │ ├── 139-2023-11-20-16-04-44.pkl │ ├── 140-2023-11-20-16-04-54.pkl │ ├── 142-2023-11-20-16-05-14.pkl │ ├── 144-2023-11-20-16-05-34.pkl │ ├── 145-2023-11-20-16-05-44.pkl │ ├── 147-2023-11-20-16-06-04.pkl │ ├── 148-2023-11-20-16-06-14.pkl │ ├── 15-2023-11-20-15-44-02.pkl │ ├── 150-2023-11-20-16-06-34.pkl │ ├── 151-2023-11-20-16-06-44.pkl │ ├── 154-2023-11-20-16-07-14.pkl │ ├── 157-2023-11-20-16-07-44.pkl │ ├── 159-2023-11-20-16-08-04.pkl │ ├── 16-2023-11-20-15-44-12.pkl │ ├── 160-2023-11-20-16-08-14.pkl │ ├── 163-2023-11-20-16-08-44.pkl │ ├── 165-2023-11-20-16-09-04.pkl │ ├── 166-2023-11-20-16-09-14.pkl │ ├── 167-2023-11-20-16-09-24.pkl │ ├── 172-2023-11-20-16-10-14.pkl │ ├── 173-2023-11-20-16-10-24.pkl │ ├── 176-2023-11-20-16-10-54.pkl │ ├── 178-2023-11-20-16-11-14.pkl │ ├── 180-2023-11-20-16-11-34.pkl │ ├── 182-2023-11-20-16-11-54.pkl │ ├── 185-2023-11-20-16-12-24.pkl │ ├── 186-2023-11-20-16-12-34.pkl │ ├── 188-2023-11-20-16-12-54.pkl │ ├── 191-2023-11-20-16-13-24.pkl │ ├── 192-2023-11-20-16-13-34.pkl │ ├── 194-2023-11-20-16-13-54.pkl │ ├── 195-2023-11-20-16-14-04.pkl │ ├── 198-2023-11-20-16-14-34.pkl │ ├── 199-2023-11-20-16-14-44.pkl │ ├── 20-2023-11-20-15-44-52.pkl │ ├── 202-2023-11-20-16-15-14.pkl │ ├── 204-2023-11-20-16-15-34.pkl │ ├── 205-2023-11-20-16-15-44.pkl │ ├── 206-2023-11-20-16-15-54.pkl │ ├── 207-2023-11-20-16-16-04.pkl │ ├── 210-2023-11-20-16-16-34.pkl │ ├── 213-2023-11-20-16-17-04.pkl │ ├── 214-2023-11-20-16-17-14.pkl │ ├── 216-2023-11-20-16-17-34.pkl │ ├── 218-2023-11-20-16-17-54.pkl │ ├── 219-2023-11-20-16-18-04.pkl │ ├── 22-2023-11-20-15-45-12.pkl │ ├── 222-2023-11-20-16-18-34.pkl │ ├── 223-2023-11-20-16-18-44.pkl │ ├── 226-2023-11-20-16-19-14.pkl │ ├── 227-2023-11-20-16-19-24.pkl │ ├── 24-2023-11-20-15-45-32.pkl │ ├── 27-2023-11-20-15-46-02.pkl │ ├── 3-2023-11-20-15-42-02.pkl │ ├── 30-2023-11-20-15-46-32.pkl │ ├── 31-2023-11-20-15-46-42.pkl │ ├── 34-2023-11-20-15-47-13.pkl │ ├── 36-2023-11-20-15-47-33.pkl │ ├── 38-2023-11-20-15-47-53.pkl │ ├── 4-2023-11-20-15-42-12.pkl │ ├── 42-2023-11-20-15-48-33.pkl │ ├── 43-2023-11-20-15-48-43.pkl │ ├── 45-2023-11-20-15-49-03.pkl │ ├── 46-2023-11-20-15-49-13.pkl │ ├── 49-2023-11-20-15-49-43.pkl │ ├── 51-2023-11-20-15-50-03.pkl │ ├── 52-2023-11-20-15-50-13.pkl │ ├── 55-2023-11-20-15-50-43.pkl │ ├── 58-2023-11-20-15-51-13.pkl │ ├── 6-2023-11-20-15-42-32.pkl │ ├── 60-2023-11-20-15-51-33.pkl │ ├── 63-2023-11-20-15-52-03.pkl │ ├── 64-2023-11-20-15-52-13.pkl │ ├── 67-2023-11-20-15-52-43.pkl │ ├── 68-2023-11-20-15-52-53.pkl │ ├── 7-2023-11-20-15-42-42.pkl │ ├── 70-2023-11-20-15-53-13.pkl │ ├── 72-2023-11-20-15-53-33.pkl │ ├── 73-2023-11-20-15-53-43.pkl │ ├── 75-2023-11-20-15-54-03.pkl │ ├── 77-2023-11-20-15-54-23.pkl │ ├── 78-2023-11-20-15-54-33.pkl │ ├── 8-2023-11-20-15-42-52.pkl │ ├── 80-2023-11-20-15-54-53.pkl │ ├── 81-2023-11-20-15-55-03.pkl │ ├── 83-2023-11-20-15-55-23.pkl │ ├── 85-2023-11-20-15-55-43.pkl │ ├── 88-2023-11-20-15-56-13.pkl │ ├── 89-2023-11-20-15-56-23.pkl │ ├── 91-2023-11-20-15-56-43.pkl │ ├── 93-2023-11-20-15-57-03.pkl │ ├── 96-2023-11-20-15-57-33.pkl │ ├── 98-2023-11-20-15-57-53.pkl │ └── 99-2023-11-20-15-58-03.pkl ├── node14_data │ ├── 100-2022-03-11-19-51-52.pkl │ ├── 103-2022-03-11-19-52-10.pkl │ ├── 104-2022-03-11-19-52-16.pkl │ ├── 105-2022-03-11-19-52-22.pkl │ ├── 107-2022-03-11-19-52-34.pkl │ ├── 108-2022-03-11-19-52-40.pkl │ ├── 11-2022-03-11-19-42-58.pkl │ ├── 112-2022-03-11-19-53-04.pkl │ ├── 113-2022-03-11-19-53-10.pkl │ ├── 115-2022-03-11-19-53-22.pkl │ ├── 117-2022-03-11-19-53-34.pkl │ ├── 118-2022-03-11-19-53-40.pkl │ ├── 120-2022-03-11-19-53-52.pkl │ ├── 121-2022-03-11-19-53-58.pkl │ ├── 122-2022-03-11-19-54-04.pkl │ ├── 123-2022-03-11-19-54-10.pkl │ ├── 125-2022-03-11-19-54-22.pkl │ ├── 126-2022-03-11-19-54-28.pkl │ ├── 127-2022-03-11-19-54-34.pkl │ ├── 128-2022-03-11-19-54-40.pkl │ ├── 129-2022-03-11-19-54-46.pkl │ ├── 130-2022-03-11-19-54-52.pkl │ ├── 131-2022-03-11-19-54-58.pkl │ ├── 132-2022-03-11-19-55-04.pkl │ ├── 135-2022-03-11-19-55-22.pkl │ ├── 136-2022-03-11-19-55-28.pkl │ ├── 137-2022-03-11-19-55-34.pkl │ ├── 138-2022-03-11-19-55-40.pkl │ ├── 14-2022-03-11-19-43-16.pkl │ ├── 149-2022-03-11-19-56-46.pkl │ ├── 150-2022-03-11-19-56-52.pkl │ ├── 152-2022-03-11-19-57-04.pkl │ ├── 167-2022-03-11-19-58-34.pkl │ ├── 168-2022-03-11-19-58-40.pkl │ ├── 169-2022-03-11-19-58-46.pkl │ ├── 174-2022-03-11-19-59-16.pkl │ ├── 176-2022-03-11-19-59-28.pkl │ ├── 177-2022-03-11-19-59-34.pkl │ ├── 178-2022-03-11-19-59-40.pkl │ ├── 180-2022-03-11-19-59-52.pkl │ ├── 182-2022-03-11-20-00-04.pkl │ ├── 183-2022-03-11-20-00-10.pkl │ ├── 185-2022-03-11-20-00-22.pkl │ ├── 186-2022-03-11-20-00-28.pkl │ ├── 187-2022-03-11-20-00-34.pkl │ ├── 188-2022-03-11-20-00-40.pkl │ ├── 189-2022-03-11-20-00-46.pkl │ ├── 190-2022-03-11-20-00-52.pkl │ ├── 191-2022-03-11-20-00-58.pkl │ ├── 192-2022-03-11-20-01-04.pkl │ ├── 194-2022-03-11-20-01-16.pkl │ ├── 195-2022-03-11-20-01-22.pkl │ ├── 196-2022-03-11-20-01-28.pkl │ ├── 199-2022-03-11-20-01-46.pkl │ ├── 20-2022-03-11-19-43-52.pkl │ ├── 202-2022-03-11-20-02-04.pkl │ ├── 203-2022-03-11-20-02-10.pkl │ ├── 208-2022-03-11-20-02-40.pkl │ ├── 210-2022-03-11-20-02-52.pkl │ ├── 211-2022-03-11-20-02-58.pkl │ ├── 212-2022-03-11-20-03-04.pkl │ ├── 225-2022-03-11-20-04-22.pkl │ ├── 226-2022-03-11-20-04-28.pkl │ ├── 231-2022-03-11-20-04-58.pkl │ ├── 232-2022-03-11-20-05-04.pkl │ ├── 24-2022-03-11-19-44-16.pkl │ ├── 240-2022-03-11-20-05-52.pkl │ ├── 244-2022-03-11-20-06-16.pkl │ ├── 259-2022-03-11-20-07-46.pkl │ ├── 261-2022-03-11-20-07-58.pkl │ ├── 262-2022-03-11-20-08-04.pkl │ ├── 263-2022-03-11-20-08-10.pkl │ ├── 264-2022-03-11-20-08-16.pkl │ ├── 266-2022-03-11-20-08-28.pkl │ ├── 267-2022-03-11-20-08-34.pkl │ ├── 27-2022-03-11-19-44-34.pkl │ ├── 270-2022-03-11-20-08-52.pkl │ ├── 271-2022-03-11-20-08-58.pkl │ ├── 272-2022-03-11-20-09-04.pkl │ ├── 273-2022-03-11-20-09-10.pkl │ ├── 274-2022-03-11-20-09-16.pkl │ ├── 276-2022-03-11-20-09-28.pkl │ ├── 277-2022-03-11-20-09-34.pkl │ ├── 279-2022-03-11-20-09-46.pkl │ ├── 28-2022-03-11-19-44-40.pkl │ ├── 286-2022-03-11-20-10-28.pkl │ ├── 29-2022-03-11-19-44-46.pkl │ ├── 291-2022-03-11-20-10-58.pkl │ ├── 293-2022-03-11-20-11-10.pkl │ ├── 297-2022-03-11-20-11-34.pkl │ ├── 30-2022-03-11-19-44-52.pkl │ ├── 31-2022-03-11-19-44-58.pkl │ ├── 32-2022-03-11-19-45-04.pkl │ ├── 33-2022-03-11-19-45-10.pkl │ ├── 34-2022-03-11-19-45-16.pkl │ ├── 35-2022-03-11-19-45-22.pkl │ ├── 36-2022-03-11-19-45-28.pkl │ ├── 37-2022-03-11-19-45-34.pkl │ ├── 38-2022-03-11-19-45-40.pkl │ ├── 40-2022-03-11-19-45-52.pkl │ ├── 42-2022-03-11-19-46-04.pkl │ ├── 44-2022-03-11-19-46-16.pkl │ ├── 46-2022-03-11-19-46-28.pkl │ ├── 48-2022-03-11-19-46-40.pkl │ ├── 50-2022-03-11-19-46-52.pkl │ ├── 51-2022-03-11-19-46-58.pkl │ ├── 52-2022-03-11-19-47-04.pkl │ ├── 53-2022-03-11-19-47-10.pkl │ ├── 54-2022-03-11-19-47-16.pkl │ ├── 56-2022-03-11-19-47-28.pkl │ ├── 69-2022-03-11-19-48-46.pkl │ ├── 70-2022-03-11-19-48-52.pkl │ ├── 71-2022-03-11-19-48-58.pkl │ ├── 72-2022-03-11-19-49-04.pkl │ ├── 74-2022-03-11-19-49-16.pkl │ ├── 79-2022-03-11-19-49-46.pkl │ ├── 82-2022-03-11-19-50-04.pkl │ ├── 90-2022-03-11-19-50-52.pkl │ ├── 95-2022-03-11-19-51-22.pkl │ └── 98-2022-03-11-19-51-40.pkl └── node21_data │ ├── 101-2023-11-24-19-04-27.pkl │ ├── 103-2023-11-24-19-04-47.pkl │ ├── 104-2023-11-24-19-04-57.pkl │ ├── 105-2023-11-24-19-05-07.pkl │ ├── 106-2023-11-24-19-05-17.pkl │ ├── 111-2023-11-24-19-06-08.pkl │ ├── 113-2023-11-24-19-06-28.pkl │ ├── 114-2023-11-24-19-06-38.pkl │ ├── 115-2023-11-24-19-06-48.pkl │ ├── 116-2023-11-24-19-06-58.pkl │ ├── 117-2023-11-24-19-07-08.pkl │ ├── 119-2023-11-24-19-07-28.pkl │ ├── 120-2023-11-24-19-07-38.pkl │ ├── 122-2023-11-24-19-07-58.pkl │ ├── 124-2023-11-24-19-08-19.pkl │ ├── 125-2023-11-24-19-08-29.pkl │ ├── 127-2023-11-24-19-08-49.pkl │ ├── 128-2023-11-24-19-08-59.pkl │ ├── 130-2023-11-24-19-09-19.pkl │ ├── 131-2023-11-24-19-09-29.pkl │ ├── 133-2023-11-24-19-09-49.pkl │ ├── 134-2023-11-24-19-09-59.pkl │ ├── 136-2023-11-24-19-10-19.pkl │ ├── 137-2023-11-24-19-10-29.pkl │ ├── 139-2023-11-24-19-10-49.pkl │ ├── 140-2023-11-24-19-11-00.pkl │ ├── 141-2023-11-24-19-11-10.pkl │ ├── 147-2023-11-24-19-12-10.pkl │ ├── 148-2023-11-24-19-12-20.pkl │ ├── 149-2023-11-24-19-12-30.pkl │ ├── 151-2023-11-24-19-12-50.pkl │ ├── 153-2023-11-24-19-13-10.pkl │ ├── 154-2023-11-24-19-13-20.pkl │ ├── 156-2023-11-24-19-13-41.pkl │ ├── 157-2023-11-24-19-13-51.pkl │ ├── 159-2023-11-24-19-14-11.pkl │ ├── 160-2023-11-24-19-14-21.pkl │ ├── 163-2023-11-24-19-14-51.pkl │ ├── 166-2023-11-24-19-15-21.pkl │ ├── 169-2023-11-24-19-15-51.pkl │ ├── 172-2023-11-24-19-16-22.pkl │ ├── 175-2023-11-24-19-16-52.pkl │ ├── 177-2023-11-24-19-17-12.pkl │ ├── 179-2023-11-24-19-17-32.pkl │ ├── 180-2023-11-24-19-17-42.pkl │ ├── 182-2023-11-24-19-18-02.pkl │ ├── 184-2023-11-24-19-18-22.pkl │ ├── 186-2023-11-24-19-18-42.pkl │ ├── 187-2023-11-24-19-18-53.pkl │ ├── 189-2023-11-24-19-19-13.pkl │ ├── 190-2023-11-24-19-19-23.pkl │ ├── 191-2023-11-24-19-19-33.pkl │ ├── 192-2023-11-24-19-19-43.pkl │ ├── 193-2023-11-24-19-19-53.pkl │ ├── 194-2023-11-24-19-20-03.pkl │ ├── 199-2023-11-24-19-20-53.pkl │ ├── 200-2023-11-24-19-21-03.pkl │ ├── 202-2023-11-24-19-21-24.pkl │ ├── 204-2023-11-24-19-21-44.pkl │ ├── 206-2023-11-24-19-22-04.pkl │ ├── 208-2023-11-24-19-22-24.pkl │ ├── 210-2023-11-24-19-22-44.pkl │ ├── 212-2023-11-24-19-23-04.pkl │ ├── 214-2023-11-24-19-23-24.pkl │ ├── 216-2023-11-24-19-23-44.pkl │ ├── 217-2023-11-24-19-23-55.pkl │ ├── 219-2023-11-24-19-24-15.pkl │ ├── 221-2023-11-24-19-24-35.pkl │ ├── 222-2023-11-24-19-24-45.pkl │ ├── 223-2023-11-24-19-24-55.pkl │ ├── 227-2023-11-24-19-25-35.pkl │ ├── 229-2023-11-24-19-25-55.pkl │ ├── 231-2023-11-24-19-26-15.pkl │ ├── 233-2023-11-24-19-26-36.pkl │ ├── 234-2023-11-24-19-26-46.pkl │ ├── 236-2023-11-24-19-27-06.pkl │ ├── 237-2023-11-24-19-27-16.pkl │ ├── 238-2023-11-24-19-27-26.pkl │ ├── 240-2023-11-24-19-27-46.pkl │ ├── 242-2023-11-24-19-28-06.pkl │ ├── 243-2023-11-24-19-28-16.pkl │ ├── 245-2023-11-24-19-28-37.pkl │ ├── 246-2023-11-24-19-28-47.pkl │ ├── 248-2023-11-24-19-29-07.pkl │ ├── 250-2023-11-24-19-29-27.pkl │ ├── 251-2023-11-24-19-29-37.pkl │ ├── 253-2023-11-24-19-29-57.pkl │ ├── 254-2023-11-24-19-30-07.pkl │ ├── 255-2023-11-24-19-30-17.pkl │ ├── 257-2023-11-24-19-30-37.pkl │ ├── 258-2023-11-24-19-30-47.pkl │ ├── 259-2023-11-24-19-30-57.pkl │ ├── 38-2023-11-24-18-53-52.pkl │ ├── 40-2023-11-24-18-54-12.pkl │ ├── 41-2023-11-24-18-54-22.pkl │ ├── 44-2023-11-24-18-54-52.pkl │ ├── 46-2023-11-24-18-55-12.pkl │ ├── 50-2023-11-24-18-55-52.pkl │ ├── 51-2023-11-24-18-56-03.pkl │ ├── 55-2023-11-24-18-56-43.pkl │ ├── 56-2023-11-24-18-56-53.pkl │ ├── 59-2023-11-24-18-57-23.pkl │ ├── 61-2023-11-24-18-57-44.pkl │ ├── 64-2023-11-24-18-58-14.pkl │ ├── 65-2023-11-24-18-58-24.pkl │ ├── 66-2023-11-24-18-58-34.pkl │ ├── 69-2023-11-24-18-59-05.pkl │ ├── 70-2023-11-24-18-59-15.pkl │ ├── 72-2023-11-24-18-59-35.pkl │ ├── 73-2023-11-24-18-59-45.pkl │ ├── 76-2023-11-24-19-00-15.pkl │ ├── 78-2023-11-24-19-00-35.pkl │ ├── 79-2023-11-24-19-00-45.pkl │ ├── 82-2023-11-24-19-01-16.pkl │ ├── 83-2023-11-24-19-01-26.pkl │ ├── 86-2023-11-24-19-01-56.pkl │ ├── 89-2023-11-24-19-02-26.pkl │ ├── 92-2023-11-24-19-02-56.pkl │ ├── 95-2023-11-24-19-03-27.pkl │ └── 97-2023-11-24-19-03-47.pkl ├── Experimental Result ├── batch_size_final_reward.png ├── csv_data │ ├── 10_nodes_bw.csv │ ├── 10_nodes_delay.csv │ ├── 10_nodes_length.csv │ ├── 10_nodes_loss.csv │ ├── 14_nodes_bw.csv │ ├── 14_nodes_delay.csv │ ├── 14_nodes_length.csv │ ├── 14_nodes_loss.csv │ ├── 21_nodes_bw.csv │ ├── 21_nodes_delay.csv │ ├── 21_nodes_length.csv │ ├── 21_nodes_loss.csv │ └── Mean variance statistics.xlsx ├── egreedy_final_reward.png ├── exp_data │ ├── 10_nodes_topo │ │ ├── bw │ │ │ ├── GA_bw.npy │ │ │ ├── Qlr_bw.npy │ │ │ ├── SCTF_bw.npy │ │ │ ├── drl_bw.npy │ │ │ ├── hdrl_bw.npy │ │ │ └── kmb_bw.npy │ │ ├── delay │ │ │ ├── GA_delay.npy │ │ │ ├── Qlr_delay.npy │ │ │ ├── SCTF_delay.npy │ │ │ ├── drl_delay.npy │ │ │ ├── hdrl_delay.npy │ │ │ └── kmb_delay.npy │ │ ├── length │ │ │ ├── GA_length.npy │ │ │ ├── Qlr_length.npy │ │ │ ├── SCTF_length.npy │ │ │ ├── drl_length.npy │ │ │ ├── hdrl_length.npy │ │ │ └── kmb_length.npy │ │ └── loss │ │ │ ├── GA_loss.npy │ │ │ ├── Qlr_loss.npy │ │ │ ├── SCTF_loss.npy │ │ │ ├── drl_loss.npy │ │ │ ├── hdrl_loss.npy │ │ │ └── kmb_loss.npy │ ├── 14_nodes_topo │ │ ├── bw │ │ │ ├── GA_bw.npy │ │ │ ├── Qlr_bw.npy │ │ │ ├── SCTF_bw.npy │ │ │ ├── drl_bw.npy │ │ │ ├── hdrl_bw.npy │ │ │ └── kmb_bw.npy │ │ ├── delay │ │ │ ├── GA_delay.npy │ │ │ ├── Qlr_delay.npy │ │ │ ├── SCTF_delay.npy │ │ │ ├── drl_delay.npy │ │ │ ├── hdrl_delay.npy │ │ │ └── kmb_delay.npy │ │ ├── length │ │ │ ├── GA_length.npy │ │ │ ├── Qlr_length.npy │ │ │ ├── SCTF_length.npy │ │ │ ├── drl_length.npy │ │ │ ├── hdrl_length.npy │ │ │ └── kmb_length.npy │ │ └── loss │ │ │ ├── GA_loss.npy │ │ │ ├── Qlr_loss.npy │ │ │ ├── SCTF_loss.npy │ │ │ ├── drl_loss.npy │ │ │ ├── hdrl_loss.npy │ │ │ └── kmb_loss.npy │ └── 21_nodes_topo │ │ ├── bw │ │ ├── GA_bw.npy │ │ ├── Qlr_bw.npy │ │ ├── SCTF_bw.npy │ │ ├── drl_bw.npy │ │ ├── hdrl_bw.npy │ │ └── kmb_bw.npy │ │ ├── delay │ │ ├── GA_delay.npy │ │ ├── Qlr_delay.npy │ │ ├── SCTF_delay.npy │ │ ├── drl_delay.npy │ │ ├── hdrl_delay.npy │ │ └── kmb_delay.npy │ │ ├── length │ │ ├── GA_length.npy │ │ ├── Qlr_length.npy │ │ ├── SCTF_length.npy │ │ ├── drl_length.npy │ │ ├── hdrl_length.npy │ │ └── kmb_length.npy │ │ └── loss │ │ ├── GA_loss.npy │ │ ├── Qlr_loss.npy │ │ ├── SCTF_loss.npy │ │ ├── drl_loss.npy │ │ ├── hdrl_loss.npy │ │ └── kmb_loss.npy ├── gamma_final_reward.png ├── images │ ├── DRL_M4MR_node10_episode_reward.png │ ├── DRL_M4MR_node14_episode_reward.png │ ├── DRL_M4MR_node21_episode_reward.png │ ├── HDRL_node10_episode_reward.png │ ├── HDRL_node14_episode_reward.png │ ├── HDRL_node21_episode_reward.png │ ├── node10_bw.png │ ├── node10_delay.png │ ├── node10_length.png │ ├── node10_loss.png │ ├── node14_bw.png │ ├── node14_delay.png │ ├── node14_length.png │ ├── node14_loss.png │ ├── node21_bw.png │ ├── node21_delay.png │ ├── node21_length.png │ └── node21_loss.png ├── lr_final_reward.png ├── reward_final_reward.png └── update_final_reward.png ├── Experimental code ├── DRL-M4MR │ ├── config.py │ ├── env_config.py │ ├── log.py │ ├── net.py │ ├── replymemory.py │ ├── rl.py │ ├── test.py │ ├── train.py │ └── z_mytest.py ├── GA │ ├── asd.py │ ├── multicast_ga1.py │ └── setting.py ├── HDRL-FNMR │ ├── HDRL-FNMR-EX │ │ ├── README.md │ │ ├── __pycache__ │ │ │ ├── constant.cpython-39.pyc │ │ │ ├── dqn.cpython-39.pyc │ │ │ ├── env.cpython-39.pyc │ │ │ ├── env_base.cpython-39.pyc │ │ │ ├── hierarchical_dqn.cpython-39.pyc │ │ │ ├── index_conversion.cpython-39.pyc │ │ │ ├── model.cpython-39.pyc │ │ │ ├── paint.cpython-39.pyc │ │ │ ├── parse_config.cpython-39.pyc │ │ │ ├── replymemory.cpython-39.pyc │ │ │ ├── reward.cpython-39.pyc │ │ │ ├── save_episode_data.cpython-39.pyc │ │ │ └── transition.cpython-39.pyc │ │ ├── compare_test.py │ │ ├── config │ │ │ ├── controller_config.yaml │ │ │ ├── controller_model_config.yaml │ │ │ ├── env_config.yaml │ │ │ ├── meta_controller_config.yaml │ │ │ ├── meta_model_config.yaml │ │ │ ├── reward_config.yaml │ │ │ └── train_config.yaml │ │ ├── constant.py │ │ ├── dqn.py │ │ ├── env_base.py │ │ ├── filter.py │ │ ├── hierarchical_dqn.py │ │ ├── index_conversion.py │ │ ├── main.py │ │ ├── model.py │ │ ├── paint.py │ │ ├── parse_config.py │ │ ├── plot_figures.py │ │ ├── replymemory.py │ │ ├── reward.py │ │ ├── save_episode_data.py │ │ └── transition.py │ └── HDRL-FNMR-IN │ │ ├── README.md │ │ ├── __pycache__ │ │ ├── constant.cpython-39.pyc │ │ ├── dqn.cpython-39.pyc │ │ ├── env.cpython-39.pyc │ │ ├── env_base.cpython-39.pyc │ │ ├── hierarchical_dqn.cpython-39.pyc │ │ ├── index_conversion.cpython-39.pyc │ │ ├── model.cpython-39.pyc │ │ ├── paint.cpython-39.pyc │ │ ├── parse_config.cpython-39.pyc │ │ ├── replymemory.cpython-39.pyc │ │ ├── reward.cpython-39.pyc │ │ ├── save_episode_data.cpython-39.pyc │ │ └── transition.cpython-39.pyc │ │ ├── compare_test.py │ │ ├── config │ │ ├── controller_config.yaml │ │ ├── controller_model_config.yaml │ │ ├── env_config.yaml │ │ ├── meta_controller_config.yaml │ │ ├── meta_model_config.yaml │ │ ├── reward_config.yaml │ │ └── train_config.yaml │ │ ├── constant.py │ │ ├── dqn.py │ │ ├── env_base.py │ │ ├── filter.py │ │ ├── hierarchical_dqn.py │ │ ├── index_conversion.py │ │ ├── main.py │ │ ├── model.py │ │ ├── paint.py │ │ ├── parse_config.py │ │ ├── plot_figures.py │ │ ├── replymemory.py │ │ ├── reward.py │ │ ├── save_episode_data.py │ │ └── transition.py ├── KMB │ └── KMB.py ├── Q_RL │ ├── Q_learning.py │ └── main.py └── SCTF │ ├── SCTF.py │ └── my_main.py ├── Notice.txt └── README.md /Experimental Dataset/node10_data/10-2023-11-20-15-43-12.pkl: 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STEP_SYMBOL = 1 26 | # BRANCH_SYMBOL = 2 27 | # 28 | # 29 | # def traverse_reward_list(reward_list): 30 | # for idx in range(len(reward_list)): 31 | # yield reward_list[idx] 32 | # 33 | # def set_rewards(rewards): 34 | # reward_default = rewards 35 | # return 36 | -------------------------------------------------------------------------------- /Experimental code/DRL-M4MR/z_mytest.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @File : z_mytest.py 3 | # @Date : 2022-11-29 4 | # @Author : chenwei -剑衣沉沉晚霞归，酒杖津津神仙来- 5 | # @From : 6 | class Test1: 7 | def __init__(self): 8 | pass 9 | 10 | def test(self, a: int): 11 | """ 12 | 13 | Args: 14 | a: 15 | 16 | Returns: 17 | 18 | """ 19 | pass 20 | -------------------------------------------------------------------------------- /Experimental code/GA/asd.py: -------------------------------------------------------------------------------- 1 | import pickle 2 | import numpy as np 3 | 4 | # f = open('./node10_data/4-2023-11-20-15-42-12.pkl','rb') 5 | # data = pickle.load(f) 6 | # print(data) 7 | # print(len(data)) 8 | import matplotlib.pyplot as plt 9 | import networkx as nx 10 | 11 | def read_pickle(pickle_path): 12 | """ 13 | 读取pickle并转化为graph 14 | :param pickle_path: 15 | :return: 16 | """ 17 | pkl_graph = nx.read_gpickle(pickle_path) 18 | print(pkl_graph.edges.data()) 19 | nx.draw(pkl_graph, with_labels=True) 20 | plt.show() 21 | return pkl_graph 22 | 23 | # data=read_pickle('node10_data/4-2023-11-20-15-42-12.pkl') 24 | 25 | data=read_pickle('node21_data/38-2023-11-24-18-53-52.pkl') 26 | -------------------------------------------------------------------------------- /Experimental code/GA/setting.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | # r,d,t,h分别表示剩余带宽、时延、丢包率、跳数的权重 4 | # r1, d1, t1, h1 = 0.25, 0.25, 0.25, 0.25 5 | r1, d1, t1, h1 = 1, 1, 1, 0 6 | 7 | m = 5 # 从dj算法的结果中挑选m个最短路径用于计算最优单播路径 8 | k = 3 # 用于计算组播的best k path 9 | 10 | # GA setting 11 | aCrossRate = 0.9 # 交叉概率 12 | aMutationRate = 0.1 # 变异概率 13 | retainRate = 0.2 # 每轮迭代时的精英保留比例 14 | aLifeCount = 20 # 每轮迭代时的种群中个体的总数 15 | iteration_num = 30 # 迭代轮数 16 | 17 | MAX_CAPACITY = 30000 # Max capacity of link (kbit/s) 18 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-EX/README.md: -------------------------------------------------------------------------------- 1 | # HDRL-FNMR 2 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-EX/__pycache__/constant.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GuetYe/DHRL-FNMR/be4301fba8711416b6f3cca46bbc09cbd0ab0404/Experimental code/HDRL-FNMR/HDRL-FNMR-EX/__pycache__/constant.cpython-39.pyc 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in_channels: state_channel 3 | out_channels: 128 4 | kernel_size: 5 | - 3 6 | - 3 7 | stride: 1 8 | padding: 0 9 | 10 | conv2: 11 | in_channels: 128 12 | out_channels: 256 13 | kernel_size: 14 | - 3 15 | - 3 16 | stride: 1 17 | padding: 0 18 | 19 | conv3: 20 | in_channels: 256 21 | out_channels: 128 22 | kernel_size: 23 | - 3 24 | - 3 25 | stride: 1 26 | padding: 0 27 | 28 | fc1: 29 | in_channels: 8192 30 | # out_channels: action_num 31 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-EX/config/env_config.yaml: -------------------------------------------------------------------------------- 1 | # symbol 2 | source_symbol: 1 3 | end_symbol: -1 4 | tree_symbol: 2 5 | branch_symbol: 3 6 | blank_symbol: 0 7 | 8 | subgoal_symbol: 4 9 | 10 | # flag 11 | done_flag: all_done 12 | part_flag: part_done 13 | step_flag: step_go_on 14 | knock_flag: knock_wall 15 | back_flag: road_back 16 | 17 | # threshold 18 | turn_on_threshold: False 19 | 20 | rollback_threshold: 2 21 | state_queue_maxlen: 3 22 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-EX/config/meta_controller_config.yaml: -------------------------------------------------------------------------------- 1 | name: extrinsic 2 | 3 | state_channels: 4 4 | action_nums: 14 5 | 6 | batch_size: 16 7 | lr: 1e-4 8 | reward_decay: 0.9 9 | tau: 1 10 | update_refquency: 10 11 | 12 | use_per: True 13 | memory_capacity: 256 14 | 15 | # e-greedy 16 | use_decay_e_greedy: True 17 | start: 1 18 | end: 0.001 19 | decay: 400 20 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-EX/config/meta_model_config.yaml: -------------------------------------------------------------------------------- 1 | conv1: 2 | # in_channels: state_channel 3 | out_channels: 128 4 | kernel_size: 5 | - 3 6 | - 3 7 | stride: 1 8 | padding: 0 9 | 10 | conv2: 11 | in_channels: 128 12 | out_channels: 256 13 | kernel_size: 14 | - 3 15 | - 3 16 | stride: 1 17 | padding: 0 18 | 19 | conv3: 20 | in_channels: 256 21 | out_channels: 128 22 | kernel_size: 23 | - 3 24 | - 3 25 | stride: 1 26 | padding: 0 27 | 28 | fc1: 29 | in_channels: 8192 30 | # out_channels: action_num 31 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-EX/config/reward_config.yaml: -------------------------------------------------------------------------------- 1 | # done_reward, step_reward, hell_reward 2 | rewards: 3 | - &done_reward 1.0 4 | - &step_reward 0.1 5 | - -0.3 # knock wall reward 6 | - -0.3 # subgoal dead reward 7 | - -0.3 # back road reward 8 | - -0.01 # step cost, not to use 9 | 10 | beta1: 1 11 | beta2: 1 12 | beta3: 1 13 | 14 | # normalizaion a to b 15 | upper_step: *step_reward 16 | lower_step: 0 17 | 18 | upper_path: *done_reward 19 | lower_path: 0 20 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-EX/config/train_config.yaml: -------------------------------------------------------------------------------- 1 | pkl_step: 5 2 | pkl_num: 1 3 | pkl_start: 0 4 | 5 | start_node: 12 6 | end_nodes: 7 | - 2 8 | - 4 9 | - 11 10 | 11 | episodes: 3000 12 | 13 | in_policy_weight_file: ./saved_agents/control-[20230324141356]_pkl12/policy_net-control-[20230324141356]_pkl12.pt 14 | ex_policy_weight_file: ./saved_agents/meta-[20230324141356]_pkl12/policy_net-meta-[20230324141356]_pkl12.pt 15 | 16 | 17 | # hqdn controller parameters config file path 18 | meta_controller_config: ./config/meta_controller_config.yaml 19 | meta_controller_model_config: ./config/meta_model_config.yaml 20 | 21 | controller_config: ./config/controller_config.yaml 22 | controller_model_config: ./config/controller_model_config.yaml 23 | 24 | # env config file path 25 | config_file: ./config/env_config.yaml 26 | reward_config_file: ./config/reward_config.yaml 27 | 28 | # data pickle file path 29 | pkl_weight_path: ./dataset 30 | 31 | data_save_path: ./data 32 | image_save_path: ./images 33 | 34 | random_seed: 1 35 | 36 | mode: train 37 | 38 | paint_intrinsic_state: False 39 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-EX/constant.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @File : constant.py 3 | # @Date : 2023-02-13 4 | # @Author : chenwei -剑衣沉沉晚霞归，酒杖津津神仙来- 5 | import numpy as np 6 | import torch 7 | 8 | EPS = np.finfo(np.float64).eps 9 | INF = np.inf 10 | DTYPE = torch.float32 11 | DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 12 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-EX/filter.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @File : filter.py 3 | # @Date : 2023-02-22 4 | # @Author : chenwei -剑衣沉沉晚霞归，酒杖津津神仙来- 5 | import os 6 | import networkx as nx 7 | from pathlib import Path 8 | import shutil 9 | 10 | def dataset_filter(pkl_dir: Path): 11 | """ 12 | Some data is wrong. For example, when the bandwidth is very low, the delay and packet loss rate are also small, 13 | resulting in conflicts. Therefore, learning difficulties will be caused every time the selection 14 | """ 15 | all_pkls = os.listdir(pkl_dir) 16 | dataset = Path('./dataset') 17 | dataset.mkdir(exist_ok=True) 18 | for p in all_pkls: 19 | pkl_file = pkl_dir / p 20 | pkl_graph = nx.read_gpickle(pkl_file) 21 | save_flag = True 22 | loss_zero = 0 23 | for n1, n2 in pkl_graph.edges: 24 | bw = pkl_graph.edges[n1, n2]['bw'] 25 | delay = pkl_graph.edges[n1, n2]['delay'] 26 | loss = pkl_graph.edges[n1, n2]['loss'] 27 | 28 | if bw == 0 and loss == 0: 29 | save_flag = False 30 | if loss != 0: 31 | loss_zero += 1 32 | 33 | if save_flag and loss_zero != 0: 34 | shutil.copyfile(pkl_file, dataset / pkl_file.name) 35 | 36 | 37 | if __name__ == '__main__': 38 | dataset_filter(Path('./NLIs')) 39 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-EX/index_conversion.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @File : index_conversion.py 3 | # @Date : 2022-11-28 4 | # @Author : chenwei -剑衣沉沉晚霞归，酒杖津津神仙来- 5 | import torch 6 | 7 | 8 | def node_to_index(*node: int): 9 | res = [] 10 | if len(node) == 1: 11 | return node[0] - 1 12 | else: 13 | for n in node: 14 | res.append(n - 1) 15 | 16 | return res 17 | 18 | 19 | def index_to_node(*index: int): 20 | 21 | if len(index) == 1: 22 | index = index[0] 23 | if isinstance(index, torch.Tensor): 24 | return int(index) + 1 25 | return index + 1 26 | else: 27 | res = [] 28 | for i in index: 29 | res.append(i + 1) 30 | 31 | return res 32 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-EX/paint.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @File : paint.py 3 | # @Date : 2023-02-13 4 | # @Author : chenwei -剑衣沉沉晚霞归，酒杖津津神仙来- 5 | import matplotlib.pyplot as plt 6 | import numpy as np 7 | from pathlib import Path 8 | import imageio 9 | 10 | 11 | # plt.style.use('seaborn-whitegrid') 12 | 13 | 14 | def paint_intrinsic_state(state, name, episode, index, step_counter): 15 | # state shape: 1, 5, 14, 14 16 | state = state.squeeze(0).numpy() # 5, 14, 14 17 | channel1 = state[0] / 255 # subgoal 18 | channel2 = state[1] / 255 # route 19 | channel3 = state[2] + state[3] + state[4] # metrics 20 | mask = np.where(channel3 != 0) 21 | 22 | channel3 = ((channel3 - channel3[mask].min()) / 23 | (channel3[mask].max() - channel3[mask].min())) 24 | channel3 -= channel3 * np.identity(14) 25 | mask = np.where(channel3 < 0) 26 | channel3[mask] = 0 27 | 28 | # RGB 29 | img = np.stack([channel2, channel1, channel3], axis=2) 30 | 31 | plt.xticks(range(14), range(1, 15)) 32 | plt.yticks(range(14), range(1, 15)) 33 | 34 | plt.title(f'episode:{episode} index:{index} step_counter:{step_counter}') 35 | plt.imshow(img, interpolation='nearest') 36 | Path(f'./images/{name}').mkdir(exist_ok=True, parents=True) 37 | plt.savefig(f'./images/{name}/{episode}_{index}_{step_counter}', ) 38 | 39 | 40 | def paint_gif(path='./images', match_str="*images/1*"): 41 | data_path = Path(path) 42 | 43 | imgs = [] 44 | for img_path in data_path.iterdir(): 45 | if img_path.match(match_str): 46 | print(img_path) 47 | img = imageio.imread(img_path) 48 | imgs.append(img) 49 | 50 | imageio.mimsave(data_path / 'test.gif', imgs, fps=30) 51 | 52 | 53 | if __name__ == '__main__': 54 | paint_gif() 55 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-EX/parse_config.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @File : parse_config.py 3 | # @Date : 2022-11-25 4 | # @Author : chenwei -剑衣沉沉晚霞归，酒杖津津神仙来- 5 | import re 6 | 7 | import yaml 8 | from pathlib import Path 9 | from easydict import EasyDict as edict 10 | 11 | loader = yaml.SafeLoader 12 | loader.add_implicit_resolver( 13 | u'tag:yaml.org,2002:float', 14 | re.compile(u'''^(?: 15 | [-+]?(?:[0-9][0-9_]*)\\.[0-9_]*(?:[eE][-+]?[0-9]+)? 16 | |[-+]?(?:[0-9][0-9_]*)(?:[eE][-+]?[0-9]+) 17 | |\\.[0-9_]+(?:[eE][-+][0-9]+)? 18 | |[-+]?[0-9][0-9_]*(?::[0-5]?[0-9])+\\.[0-9_]* 19 | |[-+]?\\.(?:inf|Inf|INF)|\\.(?:nan|NaN|NAN))$''', re.X), 20 | list(u'-+0123456789.')) 21 | 22 | 23 | def parse_config(config_path: str or Path): 24 | """parse yaml 25 | 26 | Args: 27 | config_path (Path): yaml config path, type of pathlib.Path 28 | 29 | Returns: 30 | EasyDict: a dict of config 31 | """ 32 | if isinstance(config_path, str): 33 | config_path = Path(config_path) 34 | 35 | assert Path.exists(config_path), f"{config_path} is not exists" 36 | 37 | with open(config_path, 'r') as f: 38 | config = edict(yaml.load(f, Loader=loader)) 39 | 40 | assert isinstance(config, dict), "config is not a dict type" 41 | 42 | return config 43 | 44 | 45 | def modify_config(config_path: str or Path, keyword, value): 46 | if isinstance(config_path, str): 47 | config_path = Path(config_path) 48 | 49 | with open(config_path, 'r') as f: 50 | config = yaml.load(f, Loader=loader) 51 | 52 | config[keyword] = value 53 | 54 | with open(config_path, 'w') as f: 55 | yaml.dump(config, f) 56 | 57 | 58 | if __name__ == '__main__': 59 | modify_config('./config/controller_config.yaml', 'lr', 1e-4) 60 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-EX/reward.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @File : reward.py 3 | # @Date : 2022-11-28 4 | # @Author : chenwei -剑衣沉沉晚霞归，酒杖津津神仙来- 5 | from pathlib import Path 6 | from collections import namedtuple 7 | 8 | from parse_config import parse_config 9 | from constant import EPS 10 | 11 | 12 | Limit = namedtuple('NormalizationLimit', ['upper', 'lower']) 13 | Beta = namedtuple('BetaParams', ['b1', 'b2', 'b3']) 14 | 15 | 16 | class Reward: 17 | def __init__(self, config_file: Path = Path('./config/reward_config.yaml')): 18 | 19 | config = parse_config(config_path=config_file) 20 | self.beta = Beta(config.beta1, config.beta2, config.beta3) 21 | 22 | self.limit_step = Limit(config.upper_step, config.lower_step) 23 | self.limit_path = Limit(config.upper_path, config.lower_path) 24 | 25 | self.complete, self.step, self.knock_wall, self.subgoal_dead, self.back_road, self.step_cost = config.rewards 26 | self.path_sum_reward = 0 27 | 28 | def link_func(self): 29 | def func(x, y, z): return self.beta.b1 * x \ 30 | + self.beta.b2 * (self.limit_step.upper - y) \ 31 | + self.beta.b3 * (self.limit_step.upper - z) 32 | 33 | # def func(x, y, z): return self.beta.b1 * x \ 34 | # + self.beta.b2 * (- y) \ 35 | # + self.beta.b3 * (- z) 36 | 37 | return func 38 | 39 | def path_func(self): 40 | def func(x, y, z): return self.beta.b1 * x \ 41 | + self.beta.b2 * (self.limit_path.upper - y) \ 42 | + self.beta.b3 * z 43 | 44 | # def func(x, y, z): return self.beta.b1 * x \ 45 | # + self.beta.b2 * (- y) \ 46 | # + self.beta.b3 * (- z) 47 | return func 48 | 49 | def max_min_normalize(self, x, max_v, min_v, limit: Limit): 50 | x_n = (x - min_v) / (max_v - min_v + EPS) 51 | x_hat = limit.lower + x_n * (limit.upper - limit.lower) 52 | 53 | return x_hat 54 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-EX/save_episode_data.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @File : save_episode_data.py 3 | # @Date : 2022-12-21 4 | # @Author : chenwei -剑衣沉沉晚霞归，酒杖津津神仙来- 5 | import pickle 6 | import numpy as np 7 | from collections import Counter 8 | 9 | from transition import EpisodeMetrics 10 | 11 | 12 | def save_episodes_data(data_path, episode_metrics: EpisodeMetrics): 13 | """save data of multicast tree metrics of all episodes. 14 | 15 | Args: 16 | data_path (Path): path to save 17 | episode_metrics (EpisodeMetrics): 'reward', 'subgoal_reward', 'tree_reward', 18 | 'bw', 'delay', 'loss', 'length', 'alley', 'steps' 19 | """ 20 | np.save(data_path / "episode_reward", episode_metrics.reward) 21 | np.save(data_path / "episode_subgoal_reward", episode_metrics.subgoal_reward) 22 | np.save(data_path / "episode_tree_reward", episode_metrics.tree_reward) 23 | np.save(data_path / "episode_steps", episode_metrics.steps) 24 | np.save(data_path / "episode_bw", episode_metrics.bw) 25 | np.save(data_path / "episode_delay", episode_metrics.delay) 26 | np.save(data_path / "episode_loss", episode_metrics.loss) 27 | np.save(data_path / "episode_length", episode_metrics.length) 28 | 29 | 30 | def save_episode_subgoal_counter(data_path, subgoal_counter: Counter): 31 | """save episode subgoals list to Counter 32 | 33 | Args: 34 | data_path (str): save data path 35 | subgoals (list): subgoal list 36 | """ 37 | 38 | print(f'[subgoal selected counter] {subgoal_counter}') 39 | with open(data_path / 'subgoals.pkl', 'wb+') as f: 40 | pickle.dump(subgoal_counter, f, 0) 41 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-EX/transition.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @File : transition.py 3 | # @Date : 2022-11-25 4 | # @Author : chenwei -剑衣沉沉晚霞归，酒杖津津神仙来- 5 | 6 | from collections import namedtuple 7 | 8 | # 简单的类 9 | Transition = namedtuple('Transition', 10 | ('state', 'action', 'reward', 'next_state')) 11 | 12 | EpisodeMetrics = namedtuple("EpisodeMetrics", ['reward', 'subgoal_reward', 'tree_reward', 13 | 'bw', 'delay', 'loss', 'length', 'alley', 'steps']) 14 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-IN/README.md: -------------------------------------------------------------------------------- 1 | # HDRL-FNMR 2 | 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10 11 | 12 | use_per: True 13 | memory_capacity: 512 14 | 15 | # e-greedy 16 | use_decay_e_greedy: True 17 | start: 1 18 | end: 0.001 19 | decay: 400 20 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-IN/config/controller_model_config.yaml: -------------------------------------------------------------------------------- 1 | conv1: 2 | # in_channels: state_channel 3 | out_channels: 128 4 | kernel_size: 5 | - 3 6 | - 3 7 | stride: 1 8 | padding: 0 9 | 10 | conv2: 11 | in_channels: 128 12 | out_channels: 256 13 | kernel_size: 14 | - 3 15 | - 3 16 | stride: 1 17 | padding: 0 18 | 19 | conv3: 20 | in_channels: 256 21 | out_channels: 128 22 | kernel_size: 23 | - 3 24 | - 3 25 | stride: 1 26 | padding: 0 27 | 28 | fc1: 29 | in_channels: 8192 30 | # out_channels: action_num 31 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-IN/config/env_config.yaml: -------------------------------------------------------------------------------- 1 | # symbol 2 | source_symbol: 1 3 | end_symbol: -1 4 | tree_symbol: 2 5 | branch_symbol: 3 6 | blank_symbol: 0 7 | 8 | subgoal_symbol: 4 9 | 10 | # flag 11 | done_flag: all_done 12 | part_flag: part_done 13 | step_flag: step_go_on 14 | knock_flag: knock_wall 15 | back_flag: road_back 16 | 17 | # threshold 18 | turn_on_threshold: False 19 | 20 | rollback_threshold: 2 21 | state_queue_maxlen: 3 22 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-IN/config/meta_controller_config.yaml: -------------------------------------------------------------------------------- 1 | name: extrinsic 2 | 3 | state_channels: 4 4 | action_nums: 14 5 | 6 | batch_size: 16 7 | lr: 1e-5 8 | reward_decay: 0.9 9 | tau: 1 10 | update_refquency: 100 11 | 12 | use_per: True 13 | memory_capacity: 2e3 14 | 15 | # e-greedy 16 | use_decay_e_greedy: True 17 | start: 1 18 | end: 0.001 19 | decay: 400 20 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-IN/config/meta_model_config.yaml: -------------------------------------------------------------------------------- 1 | conv1: 2 | # in_channels: state_channel 3 | out_channels: 128 4 | kernel_size: 5 | - 3 6 | - 3 7 | stride: 1 8 | padding: 0 9 | 10 | conv2: 11 | in_channels: 128 12 | out_channels: 256 13 | kernel_size: 14 | - 3 15 | - 3 16 | stride: 1 17 | padding: 0 18 | 19 | conv3: 20 | in_channels: 256 21 | out_channels: 128 22 | kernel_size: 23 | - 3 24 | - 3 25 | stride: 1 26 | padding: 0 27 | 28 | fc1: 29 | in_channels: 8192 30 | # out_channels: action_num 31 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-IN/config/reward_config.yaml: -------------------------------------------------------------------------------- 1 | # done_reward, step_reward, hell_reward 2 | rewards: 3 | - &done_reward 1.0 4 | - &step_reward 0.1 5 | - -0.5 # knock wall reward 6 | - -0.1 # subgoal dead reward 7 | - -0.5 # back road reward 8 | - -0.01 # step cost, not to use 9 | 10 | beta1: 1 11 | beta2: 1 12 | beta3: 1 13 | 14 | # normalizaion a to b 15 | upper_step: *step_reward 16 | lower_step: 0 17 | 18 | upper_path: *done_reward 19 | lower_path: 0 20 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-IN/config/train_config.yaml: -------------------------------------------------------------------------------- 1 | pkl_step: 3 2 | pkl_num: 1 3 | pkl_start: 0 4 | 5 | start_node: 12 6 | end_nodes: 7 | # - 2 8 | - 4 9 | # - 11 10 | 11 | episodes: 3000 12 | 13 | in_policy_weight_file: ./saved_agents/control-[20230322151712]_pkl12/policy_net-control-[20230322151712]_pkl12.pt 14 | ex_policy_weight_file: ./saved_agents/meta-[20230322151712]_pkl12/policy_net-meta-[20230322151712]_pkl12.pt 15 | 16 | # hqdn controller parameters config file path 17 | meta_controller_config: ./config/meta_controller_config.yaml 18 | meta_controller_model_config: ./config/meta_model_config.yaml 19 | 20 | controller_config: ./config/controller_config.yaml 21 | controller_model_config: ./config/controller_model_config.yaml 22 | 23 | # env config file path 24 | config_file: ./config/env_config.yaml 25 | reward_config_file: ./config/reward_config.yaml 26 | 27 | # data pickle file path 28 | pkl_weight_path: ./dataset 29 | 30 | data_save_path: ./data 31 | image_save_path: ./images 32 | 33 | random_seed: 1 34 | 35 | mode: train 36 | 37 | paint_intrinsic_state: False 38 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-IN/constant.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @File : constant.py 3 | # @Date : 2023-02-13 4 | # @Author : chenwei -剑衣沉沉晚霞归，酒杖津津神仙来- 5 | import numpy as np 6 | import torch 7 | 8 | EPS = np.finfo(np.float64).eps 9 | INF = np.inf 10 | DTYPE = torch.float32 11 | DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 12 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-IN/filter.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @File : filter.py 3 | # @Date : 2023-02-22 4 | # @Author : chenwei -剑衣沉沉晚霞归，酒杖津津神仙来- 5 | import os 6 | import networkx as nx 7 | from pathlib import Path 8 | import shutil 9 | 10 | def dataset_filter(pkl_dir: Path): 11 | """ 12 | Some data is wrong. For example, when the bandwidth is very low, the delay and packet loss rate are also small, 13 | resulting in conflicts. Therefore, learning difficulties will be caused every time the selection 14 | """ 15 | all_pkls = os.listdir(pkl_dir) 16 | dataset = Path('./dataset') 17 | dataset.mkdir(exist_ok=True) 18 | for p in all_pkls: 19 | pkl_file = pkl_dir / p 20 | pkl_graph = nx.read_gpickle(pkl_file) 21 | save_flag = True 22 | loss_zero = 0 23 | for n1, n2 in pkl_graph.edges: 24 | bw = pkl_graph.edges[n1, n2]['bw'] 25 | delay = pkl_graph.edges[n1, n2]['delay'] 26 | loss = pkl_graph.edges[n1, n2]['loss'] 27 | 28 | if bw == 0 and loss == 0: 29 | save_flag = False 30 | if loss != 0: 31 | loss_zero += 1 32 | 33 | if save_flag and loss_zero != 0: 34 | shutil.copyfile(pkl_file, dataset / pkl_file.name) 35 | 36 | 37 | if __name__ == '__main__': 38 | dataset_filter(Path('./NLIs')) 39 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-IN/index_conversion.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @File : index_conversion.py 3 | # @Date : 2022-11-28 4 | # @Author : chenwei -剑衣沉沉晚霞归，酒杖津津神仙来- 5 | import torch 6 | 7 | 8 | def node_to_index(*node: int): 9 | res = [] 10 | if len(node) == 1: 11 | return node[0] - 1 12 | else: 13 | for n in node: 14 | res.append(n - 1) 15 | 16 | return res 17 | 18 | 19 | def index_to_node(*index: int): 20 | 21 | if len(index) == 1: 22 | index = index[0] 23 | if isinstance(index, torch.Tensor): 24 | return int(index) + 1 25 | return index + 1 26 | else: 27 | res = [] 28 | for i in index: 29 | res.append(i + 1) 30 | 31 | return res 32 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-IN/paint.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @File : paint.py 3 | # @Date : 2023-02-13 4 | # @Author : chenwei -剑衣沉沉晚霞归，酒杖津津神仙来- 5 | import matplotlib.pyplot as plt 6 | import numpy as np 7 | from pathlib import Path 8 | import imageio 9 | 10 | 11 | # plt.style.use('seaborn-whitegrid') 12 | 13 | 14 | def paint_intrinsic_state(state, name, episode, index, step_counter): 15 | # state shape: 1, 5, 14, 14 16 | state = state.squeeze(0).numpy() # 5, 14, 14 17 | channel1 = state[0] / 255 # subgoal 18 | channel2 = state[1] / 255 # route 19 | channel3 = state[2] + state[3] + state[4] # metrics 20 | mask = np.where(channel3 != 0) 21 | 22 | channel3 = ((channel3 - channel3[mask].min()) / 23 | (channel3[mask].max() - channel3[mask].min())) 24 | channel3 -= channel3 * np.identity(14) 25 | mask = np.where(channel3 < 0) 26 | channel3[mask] = 0 27 | 28 | # RGB 29 | img = np.stack([channel2, channel1, channel3], axis=2) 30 | 31 | plt.xticks(range(14), range(1, 15)) 32 | plt.yticks(range(14), range(1, 15)) 33 | 34 | plt.title(f'episode:{episode} index:{index} step_counter:{step_counter}') 35 | plt.imshow(img, interpolation='nearest') 36 | Path(f'./images/{name}').mkdir(exist_ok=True, parents=True) 37 | plt.savefig(f'./images/{name}/{episode}_{index}_{step_counter}', ) 38 | 39 | 40 | def paint_gif(path='./images', match_str="*images/1*"): 41 | data_path = Path(path) 42 | 43 | imgs = [] 44 | for img_path in data_path.iterdir(): 45 | if img_path.match(match_str): 46 | print(img_path) 47 | img = imageio.imread(img_path) 48 | imgs.append(img) 49 | 50 | imageio.mimsave(data_path / 'test.gif', imgs, fps=30) 51 | 52 | 53 | if __name__ == '__main__': 54 | paint_gif() 55 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-IN/parse_config.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @File : parse_config.py 3 | # @Date : 2022-11-25 4 | # @Author : chenwei -剑衣沉沉晚霞归，酒杖津津神仙来- 5 | import re 6 | 7 | import yaml 8 | from pathlib import Path 9 | from easydict import EasyDict as edict 10 | 11 | loader = yaml.SafeLoader 12 | loader.add_implicit_resolver( 13 | u'tag:yaml.org,2002:float', 14 | re.compile(u'''^(?: 15 | [-+]?(?:[0-9][0-9_]*)\\.[0-9_]*(?:[eE][-+]?[0-9]+)? 16 | |[-+]?(?:[0-9][0-9_]*)(?:[eE][-+]?[0-9]+) 17 | |\\.[0-9_]+(?:[eE][-+][0-9]+)? 18 | |[-+]?[0-9][0-9_]*(?::[0-5]?[0-9])+\\.[0-9_]* 19 | |[-+]?\\.(?:inf|Inf|INF)|\\.(?:nan|NaN|NAN))$''', re.X), 20 | list(u'-+0123456789.')) 21 | 22 | 23 | def parse_config(config_path: str or Path): 24 | """parse yaml 25 | 26 | Args: 27 | config_path (Path): yaml config path, type of pathlib.Path 28 | 29 | Returns: 30 | EasyDict: a dict of config 31 | """ 32 | if isinstance(config_path, str): 33 | config_path = Path(config_path) 34 | 35 | assert Path.exists(config_path), f"{config_path} is not exists" 36 | 37 | with open(config_path, 'r') as f: 38 | config = edict(yaml.load(f, Loader=loader)) 39 | 40 | assert isinstance(config, dict), "config is not a dict type" 41 | 42 | return config 43 | 44 | 45 | def modify_config(config_path: str or Path, keyword, value): 46 | if isinstance(config_path, str): 47 | config_path = Path(config_path) 48 | 49 | with open(config_path, 'r') as f: 50 | config = yaml.load(f, Loader=loader) 51 | 52 | config[keyword] = value 53 | 54 | with open(config_path, 'w') as f: 55 | yaml.dump(config, f) 56 | 57 | 58 | if __name__ == '__main__': 59 | modify_config('./config/controller_config.yaml', 'lr', 1e-4) 60 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-IN/reward.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @File : reward.py 3 | # @Date : 2022-11-28 4 | # @Author : chenwei -剑衣沉沉晚霞归，酒杖津津神仙来- 5 | from pathlib import Path 6 | from collections import namedtuple 7 | 8 | from parse_config import parse_config 9 | from constant import EPS 10 | 11 | 12 | Limit = namedtuple('NormalizationLimit', ['upper', 'lower']) 13 | Beta = namedtuple('BetaParams', ['b1', 'b2', 'b3']) 14 | 15 | 16 | class Reward: 17 | def __init__(self, config_file: Path = Path('./config/reward_config.yaml')): 18 | 19 | config = parse_config(config_path=config_file) 20 | self.beta = Beta(config.beta1, config.beta2, config.beta3) 21 | 22 | self.limit_step = Limit(config.upper_step, config.lower_step) 23 | self.limit_path = Limit(config.upper_path, config.lower_path) 24 | 25 | self.complete, self.step, self.knock_wall, self.subgoal_dead, self.back_road, self.step_cost = config.rewards 26 | self.path_sum_reward = 0 27 | 28 | def link_func(self): 29 | def func(x, y, z): return self.beta.b1 * x \ 30 | + self.beta.b2 * (self.limit_step.upper - y) \ 31 | + self.beta.b3 * (self.limit_step.upper - z) 32 | 33 | # def func(x, y, z): return self.beta.b1 * x \ 34 | # + self.beta.b2 * (- y) \ 35 | # + self.beta.b3 * (- z) 36 | 37 | return func 38 | 39 | def path_func(self): 40 | def func(x, y, z): return self.beta.b1 * x \ 41 | + self.beta.b2 * (self.limit_path.upper - y) \ 42 | + self.beta.b3 * z 43 | 44 | # def func(x, y, z): return self.beta.b1 * x \ 45 | # + self.beta.b2 * (- y) \ 46 | # + self.beta.b3 * (- z) 47 | return func 48 | 49 | def max_min_normalize(self, x, max_v, min_v, limit: Limit): 50 | x_n = (x - min_v) / (max_v - min_v + EPS) 51 | x_hat = limit.lower + x_n * (limit.upper - limit.lower) 52 | 53 | return x_hat 54 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-IN/save_episode_data.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @File : save_episode_data.py 3 | # @Date : 2022-12-21 4 | # @Author : chenwei -剑衣沉沉晚霞归，酒杖津津神仙来- 5 | import pickle 6 | import numpy as np 7 | from collections import Counter 8 | 9 | from transition import EpisodeMetrics 10 | 11 | 12 | def save_episodes_data(data_path, episode_metrics: EpisodeMetrics): 13 | """save data of multicast tree metrics of all episodes. 14 | 15 | Args: 16 | data_path (Path): path to save 17 | episode_metrics (EpisodeMetrics): 'reward', 'subgoal_reward', 'tree_reward', 18 | 'bw', 'delay', 'loss', 'length', 'alley', 'steps' 19 | """ 20 | np.save(data_path / "episode_reward", episode_metrics.reward) 21 | np.save(data_path / "episode_subgoal_reward", episode_metrics.subgoal_reward) 22 | np.save(data_path / "episode_tree_reward", episode_metrics.tree_reward) 23 | np.save(data_path / "episode_steps", episode_metrics.steps) 24 | np.save(data_path / "episode_bw", episode_metrics.bw) 25 | np.save(data_path / "episode_delay", episode_metrics.delay) 26 | np.save(data_path / "episode_loss", episode_metrics.loss) 27 | np.save(data_path / "episode_length", episode_metrics.length) 28 | 29 | 30 | def save_episode_subgoal_counter(data_path, subgoal_counter: Counter): 31 | """save episode subgoals list to Counter 32 | 33 | Args: 34 | data_path (str): save data path 35 | subgoals (list): subgoal list 36 | """ 37 | 38 | print(f'[subgoal selected counter] {subgoal_counter}') 39 | with open(data_path / 'subgoals.pkl', 'wb+') as f: 40 | pickle.dump(subgoal_counter, f, 0) 41 | -------------------------------------------------------------------------------- /Experimental code/HDRL-FNMR/HDRL-FNMR-IN/transition.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @File : transition.py 3 | # @Date : 2022-11-25 4 | # @Author : chenwei -剑衣沉沉晚霞归，酒杖津津神仙来- 5 | 6 | from collections import namedtuple 7 | 8 | # 简单的类 9 | Transition = namedtuple('Transition', 10 | ('state', 'action', 'reward', 'next_state')) 11 | 12 | EpisodeMetrics = namedtuple("EpisodeMetrics", ['reward', 'subgoal_reward', 'tree_reward', 13 | 'bw', 'delay', 'loss', 'length', 'alley', 'steps']) 14 | -------------------------------------------------------------------------------- /Notice.txt: -------------------------------------------------------------------------------- 1 | Dear Researcher 2 | Thank you for your interest in our work. 3 | However, since we found that in 2024, a research group of graduate students used our open source code to participate in the China ## Network Technology Competition and won a prize, with almost no changes to our work and code, and in 2023, when we reviewed a manuscript for some journal, we found that the manuscript submitted by a graduate student from Sichuan Province also had stolen manuscripts and codes with almost no changes. Therefore, we withdrew all the codes from publication and only some of them were made available. The interested research groups could reimplement our code according to the detailed steps introduced in our paper. 4 | We strongly condemn this immoral act. 5 | Authors. 6 | November, 2024 7 | 8 | 9 | 声明 10 | 11 | 由于发现24年有研究课题小组的研究生用我们开源的代码参加中国##学生网络技术比赛并获奖，几乎没有任何的内容设计和代码上的改动，我们课题组在23年为某一中文核心期刊审稿时发现四川省某高校的一个研究生的投稿稿件也是盗用了我们课题组未上网的中文稿件和代码，因此我们暂停公开所有代码，有兴趣的课题组可以根据我们论文介绍的详细步骤实现我们的代码。 12 | 我们严厉谴责这种不道德的行为。 13 | 2014年11月 -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Experiment data 2 | --------------------------------------------------------------------------------