Learning Transferable Skills in Action RPGs via Directed Skill Graphs and Selective Adaptation
Fuente:
arXiv
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912959856377856 |
|---|---|
| author | Najar, Ali |
| author_facet | Najar, Ali |
| contents | Lifelong agents should expand their competence over time without retraining from scratch or overwriting previously learned behaviors. We investigate this in a challenging real-time control setting (Dark Souls III) by representing combat as a directed skill graph and training its components in a hierarchical curriculum. The resulting agent decomposes control into five reusable skills: camera control, target lock-on, movement, dodging, and a heal-attack decision policy, each optimized for a narrow responsibility. This factorization improves sample efficiency by reducing the burden on any single policy and supports selective post-training: when the environment shifts from Phase 1 to Phase 2, only a subset of skills must be adapted, while upstream skills remain transferable. Empirically, we find that targeted fine-tuning of just two skills rapidly recovers performance under a limited interaction budget, suggesting that skill-graph curricula together with selective fine-tuning offer a practical pathway toward evolving, continually learning agents in complex real-time environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17923 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Transferable Skills in Action RPGs via Directed Skill Graphs and Selective Adaptation Najar, Ali Artificial Intelligence Lifelong agents should expand their competence over time without retraining from scratch or overwriting previously learned behaviors. We investigate this in a challenging real-time control setting (Dark Souls III) by representing combat as a directed skill graph and training its components in a hierarchical curriculum. The resulting agent decomposes control into five reusable skills: camera control, target lock-on, movement, dodging, and a heal-attack decision policy, each optimized for a narrow responsibility. This factorization improves sample efficiency by reducing the burden on any single policy and supports selective post-training: when the environment shifts from Phase 1 to Phase 2, only a subset of skills must be adapted, while upstream skills remain transferable. Empirically, we find that targeted fine-tuning of just two skills rapidly recovers performance under a limited interaction budget, suggesting that skill-graph curricula together with selective fine-tuning offer a practical pathway toward evolving, continually learning agents in complex real-time environments. |
| title | Learning Transferable Skills in Action RPGs via Directed Skill Graphs and Selective Adaptation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.17923 |