000			01860nam a22001697a 4500
003			NUST
082			_a005.1,KAM
100			_aKamal, Muhammad Haider _9112571
245			_aReplication of Multi-Agent Reinforcement Learning for “Hide & Seek” Problem / _cMuhammad Haider Kamal,
264			_aRawalpindi _bMCS, NUST _c2023
300			_aviii, 79 p
505			_aReinforcement 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			_aMSCSE / MSSE-27 _9112568
690			_bMSCSE / MSSE _9112573
700			_aSupervisor Dr. Muaz Ahmed Khan Niazi _9112572
942			_2ddc _cTHE
999			_c594849 _d594849