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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2507.16434 |
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| _version_ | 1866912496582918144 |
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| author | Patsantzis, Stassa |
| author_facet | Patsantzis, Stassa |
| contents | A "model" is a theory that describes the state of an environment and the effects of an agent's decisions on the environment. A model-based agent can use its model to predict the effects of its future actions and so plan ahead, but must know the state of the environment. A model-free agent cannot plan, but can act without a model and without completely observing the environment. An autonomous agent capable of acting independently in novel environments must combine both sets of capabilities. We show how to create such an agent with Meta-Interpretive Learning used to learn a model-based Solver used to train a model-free Controller that can solve the same planning problems as the Solver. We demonstrate the equivalence in problem-solving ability of the two agents on grid navigation problems in two kinds of environment: randomly generated mazes, and lake maps with wide open areas. We find that all navigation problems solved by the Solver are also solved by the Controller, indicating the two are equivalent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16434 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From model-based learning to model-free behaviour with Meta-Interpretive Learning Patsantzis, Stassa Artificial Intelligence Machine Learning A "model" is a theory that describes the state of an environment and the effects of an agent's decisions on the environment. A model-based agent can use its model to predict the effects of its future actions and so plan ahead, but must know the state of the environment. A model-free agent cannot plan, but can act without a model and without completely observing the environment. An autonomous agent capable of acting independently in novel environments must combine both sets of capabilities. We show how to create such an agent with Meta-Interpretive Learning used to learn a model-based Solver used to train a model-free Controller that can solve the same planning problems as the Solver. We demonstrate the equivalence in problem-solving ability of the two agents on grid navigation problems in two kinds of environment: randomly generated mazes, and lake maps with wide open areas. We find that all navigation problems solved by the Solver are also solved by the Controller, indicating the two are equivalent. |
| title | From model-based learning to model-free behaviour with Meta-Interpretive Learning |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.16434 |