Reinforcement Learning for Constraint Satisfaction Game Agents
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2026
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| _version_ | 1866901798419169280 |
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| author | Praveen, Amritesh Venkat, Gagan |
| author_facet | Praveen, Amritesh Venkat, Gagan |
| contents | <p>This work explores reinforcement learning for constraint satisfaction game environments, progressing from tabular Q-learning and DQN experiments to an adversarial PPO-based Wumpus agent. The system transforms the traditional static Wumpus into an adaptive RL-driven adversary capable of learning pursuit behaviour through scent-memory tracking and reward shaping. The project includes a full-stack deployment using FastAPI, React, Firebase, and Gymnasium-compatible training environments.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20076630 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Reinforcement Learning for Constraint Satisfaction Game Agents Praveen, Amritesh Venkat, Gagan <p>This work explores reinforcement learning for constraint satisfaction game environments, progressing from tabular Q-learning and DQN experiments to an adversarial PPO-based Wumpus agent. The system transforms the traditional static Wumpus into an adaptive RL-driven adversary capable of learning pursuit behaviour through scent-memory tracking and reward shaping. The project includes a full-stack deployment using FastAPI, React, Firebase, and Gymnasium-compatible training environments.</p> |
| title | Reinforcement Learning for Constraint Satisfaction Game Agents |
| url | https://doi.org/10.5281/zenodo.20076630 |