Replication of Multi-Agent Reinforcement Learning for “Hide & Seek” Problem / (Record no. 594849)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01860nam a22001697a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | NUST |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.1,KAM |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Kamal, Muhammad Haider |
| 245 ## - TITLE STATEMENT | |
| Title | Replication of Multi-Agent Reinforcement Learning for “Hide & Seek” Problem / |
| Statement of responsibility, etc. | Muhammad Haider Kamal, |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Rawalpindi |
| Name of producer, publisher, distributor, manufacturer | MCS, NUST |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | viii, 79 p |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Reinforcement learning generates policies based on reward functions and hyperparameters. Slight changes in these can significantly affect results. The lack of documentation and reproducibility in Reinforcement learning research makes it difficult to replicate once-deduced strategies. While previous research has identified strategies using grounded maneuver, there is limited work in the more complex environments. The agents in this study are simulated similarly to Open Al’s hide and seek agents, in addition to a flying mechanism, enhancing their mobility, and expanding their range of possible actions and strategies. This added functionality improves the Hider agents to develop chasing strategy from approximately 2 million steps to 1.6 million steps and hiders shelter strategy from approximately 25 million steps to 2.3 million steps while using a smaller batch size of 3072 instead of 64000. We also discuss the importance of reward functions design and deployment in a curriculum-based environment to encourage agents to learn basic skills along with the challenges in replicating these Reinforcement learning strategies. We demonstrated that the results of the reinforcement agent can be replicated in more complex environment and similar strategies are evolved including” running and chasing” and ”fort building”. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MSCSE / MSSE-27 |
| 690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN) | |
| Topical term following geographic name as entry element | MSCSE / MSSE |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor Dr. Muaz Ahmed Khan Niazi |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| Military College of Signals (MCS) | Military College of Signals (MCS) | Thesis | 05/25/2023 | 005.1,KAM | MCSTCS-544 | Thesis |