Optimization Strategies for Atari Game Environments: Integrating Snake Optimization Algorithm and Energy Valley Optimization in Reinforcement Learning Models
One of the biggest problems in gaming AI is related to how we can optimize and adapt a deep reinforcement learning (DRL) model, especially when it is running inside complex, dynamic environments like “PacMan”. The existing research has concentrated more or less on basic DRL approaches though the uti...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-07-01
|
| Series: | AI |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-2688/5/3/57 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850258750212931584 |
|---|---|
| author | Sadeq Mohammed Kadhm Sarkhi Hakan Koyuncu |
| author_facet | Sadeq Mohammed Kadhm Sarkhi Hakan Koyuncu |
| author_sort | Sadeq Mohammed Kadhm Sarkhi |
| collection | DOAJ |
| description | One of the biggest problems in gaming AI is related to how we can optimize and adapt a deep reinforcement learning (DRL) model, especially when it is running inside complex, dynamic environments like “PacMan”. The existing research has concentrated more or less on basic DRL approaches though the utilization of advanced optimization methods. This paper tries to fill these gaps by proposing an innovative methodology that combines DRL with high-level metaheuristic optimization methods. The work presented in this paper specifically refactors DRL models on the “PacMan” domain with Energy Serpent Optimizer (ESO) for hyperparameter search. These novel adaptations give a major performance boost to the AI agent, as these are where its adaptability, response time, and efficiency gains start actually showing in the more complex game space. This work innovatively incorporates the metaheuristic optimization algorithm into another field—DRL—for Atari gaming AI. This integration is essential for the improvement of DRL models in general and allows for more efficient and real-time game play. This work delivers a comprehensive empirical study for these algorithms that not only verifies their capabilities in practice but also sets a state of the art through the prism of AI-driven game development. More than simply improving gaming AI, the developments could eventually apply to more sophisticated gaming environments, ongoing improvement of algorithms during execution, real-time adaptation regarding learning, and likely even robotics/autonomous systems. This study further illustrates the necessity for even-handed and conscientious application of AI in gaming—specifically regarding questions of fairness and addiction. |
| format | Article |
| id | doaj-art-2c79a9f1bb0040a590f0297ff2cfee98 |
| institution | OA Journals |
| issn | 2673-2688 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-2c79a9f1bb0040a590f0297ff2cfee982025-08-20T01:56:02ZengMDPI AGAI2673-26882024-07-01531172119110.3390/ai5030057Optimization Strategies for Atari Game Environments: Integrating Snake Optimization Algorithm and Energy Valley Optimization in Reinforcement Learning ModelsSadeq Mohammed Kadhm Sarkhi0Hakan Koyuncu1Electrical and Computer Engineering Department, Altinbas University, 34200 Istanbul, TurkeyComputer Engineering Department, Altinbas University, 34217 Istanbul, TurkeyOne of the biggest problems in gaming AI is related to how we can optimize and adapt a deep reinforcement learning (DRL) model, especially when it is running inside complex, dynamic environments like “PacMan”. The existing research has concentrated more or less on basic DRL approaches though the utilization of advanced optimization methods. This paper tries to fill these gaps by proposing an innovative methodology that combines DRL with high-level metaheuristic optimization methods. The work presented in this paper specifically refactors DRL models on the “PacMan” domain with Energy Serpent Optimizer (ESO) for hyperparameter search. These novel adaptations give a major performance boost to the AI agent, as these are where its adaptability, response time, and efficiency gains start actually showing in the more complex game space. This work innovatively incorporates the metaheuristic optimization algorithm into another field—DRL—for Atari gaming AI. This integration is essential for the improvement of DRL models in general and allows for more efficient and real-time game play. This work delivers a comprehensive empirical study for these algorithms that not only verifies their capabilities in practice but also sets a state of the art through the prism of AI-driven game development. More than simply improving gaming AI, the developments could eventually apply to more sophisticated gaming environments, ongoing improvement of algorithms during execution, real-time adaptation regarding learning, and likely even robotics/autonomous systems. This study further illustrates the necessity for even-handed and conscientious application of AI in gaming—specifically regarding questions of fairness and addiction.https://www.mdpi.com/2673-2688/5/3/57SOAEVOreinforcement learningmetaheuristicagentESO |
| spellingShingle | Sadeq Mohammed Kadhm Sarkhi Hakan Koyuncu Optimization Strategies for Atari Game Environments: Integrating Snake Optimization Algorithm and Energy Valley Optimization in Reinforcement Learning Models AI SOA EVO reinforcement learning metaheuristic agent ESO |
| title | Optimization Strategies for Atari Game Environments: Integrating Snake Optimization Algorithm and Energy Valley Optimization in Reinforcement Learning Models |
| title_full | Optimization Strategies for Atari Game Environments: Integrating Snake Optimization Algorithm and Energy Valley Optimization in Reinforcement Learning Models |
| title_fullStr | Optimization Strategies for Atari Game Environments: Integrating Snake Optimization Algorithm and Energy Valley Optimization in Reinforcement Learning Models |
| title_full_unstemmed | Optimization Strategies for Atari Game Environments: Integrating Snake Optimization Algorithm and Energy Valley Optimization in Reinforcement Learning Models |
| title_short | Optimization Strategies for Atari Game Environments: Integrating Snake Optimization Algorithm and Energy Valley Optimization in Reinforcement Learning Models |
| title_sort | optimization strategies for atari game environments integrating snake optimization algorithm and energy valley optimization in reinforcement learning models |
| topic | SOA EVO reinforcement learning metaheuristic agent ESO |
| url | https://www.mdpi.com/2673-2688/5/3/57 |
| work_keys_str_mv | AT sadeqmohammedkadhmsarkhi optimizationstrategiesforatarigameenvironmentsintegratingsnakeoptimizationalgorithmandenergyvalleyoptimizationinreinforcementlearningmodels AT hakankoyuncu optimizationstrategiesforatarigameenvironmentsintegratingsnakeoptimizationalgorithmandenergyvalleyoptimizationinreinforcementlearningmodels |