Pokemon Red via Reinforcement Learning
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912269248495616 |
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| author | Pleines, Marco Addis, Daniel Rubinstein, David Zimmer, Frank Preuss, Mike Whidden, Peter |
| author_facet | Pleines, Marco Addis, Daniel Rubinstein, David Zimmer, Frank Preuss, Mike Whidden, Peter |
| contents | Pokémon Red, a classic Game Boy JRPG, presents significant challenges as a testbed for agents, including multi-tasking, long horizons of tens of thousands of steps, hard exploration, and a vast array of potential policies. We introduce a simplistic environment and a Deep Reinforcement Learning (DRL) training methodology, demonstrating a baseline agent that completes an initial segment of the game up to completing Cerulean City. Our experiments include various ablations that reveal vulnerabilities in reward shaping, where agents exploit specific reward signals. We also discuss limitations and argue that games like Pokémon hold strong potential for future research on Large Language Model agents, hierarchical training algorithms, and advanced exploration methods. Source Code: https://github.com/MarcoMeter/neroRL/tree/poke_red |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19920 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pokemon Red via Reinforcement Learning Pleines, Marco Addis, Daniel Rubinstein, David Zimmer, Frank Preuss, Mike Whidden, Peter Machine Learning Pokémon Red, a classic Game Boy JRPG, presents significant challenges as a testbed for agents, including multi-tasking, long horizons of tens of thousands of steps, hard exploration, and a vast array of potential policies. We introduce a simplistic environment and a Deep Reinforcement Learning (DRL) training methodology, demonstrating a baseline agent that completes an initial segment of the game up to completing Cerulean City. Our experiments include various ablations that reveal vulnerabilities in reward shaping, where agents exploit specific reward signals. We also discuss limitations and argue that games like Pokémon hold strong potential for future research on Large Language Model agents, hierarchical training algorithms, and advanced exploration methods. Source Code: https://github.com/MarcoMeter/neroRL/tree/poke_red |
| title | Pokemon Red via Reinforcement Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.19920 |