Action Model Learning with Guarantees

• URL: http://arxiv.org/abs/2404.09631v1
• Date: Mon, 15 Apr 2024 10:01:43 GMT
• Title: Action Model Learning with Guarantees
• Authors: Diego Aineto, Enrico Scala,
• Abstract summary: We develop a theory for action model learning based on version spaces that interprets the task as search for hypothesis that are consistent with the learning examples. Our theoretical findings are instantiated in an online algorithm that maintains a compact representation of all solutions of the problem.
• Score: 5.524804393257921
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper studies the problem of action model learning with full observability. Following the learning by search paradigm by Mitchell, we develop a theory for action model learning based on version spaces that interprets the task as search for hypothesis that are consistent with the learning examples. Our theoretical findings are instantiated in an online algorithm that maintains a compact representation of all solutions of the problem. Among these range of solutions, we bring attention to actions models approximating the actual transition system from below (sound models) and from above (complete models). We show how to manipulate the output of our learning algorithm to build deterministic and non-deterministic formulations of the sound and complete models and prove that, given enough examples, both formulations converge into the very same true model. Our experiments reveal their usefulness over a range of planning domains.

Related papers

• Hint assisted reinforcement learning: an application in radio astronomy [2.4366811507669115]
  We propose to use hints generated by the environment as an aid to the reinforcement learning process mitigating the complexity of model construction. Results in several environments show that we get the increased sample efficiency by using hints as compared to model free methods.
  arXiv  Detail & Related papers  (2023-01-10T12:24:13Z)
• Model-agnostic multi-objective approach for the evolutionary discovery of mathematical models [55.41644538483948]
  In modern data science, it is more interesting to understand the properties of the model, which parts could be replaced to obtain better results. We use multi-objective evolutionary optimization for composite data-driven model learning to obtain the algorithm's desired properties.
  arXiv  Detail & Related papers  (2021-07-07T11:17:09Z)
• Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation [3.728946517493471]
  MEEE is a model-ensemble method that consists of optimistic exploration and weighted exploitation. Our approach outperforms other model-free and model-based state-of-the-art methods, especially in sample complexity.
  arXiv  Detail & Related papers  (2021-07-05T07:18:20Z)
• Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations [64.85696493596821]
  In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction. We propose a counterfactual method that learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss. Our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-03-18T12:57:34Z)
• Model-free Representation Learning and Exploration in Low-rank MDPs [64.72023662543363]
  We present the first model-free representation learning algorithms for low rank MDPs. Key algorithmic contribution is a new minimax representation learning objective. Result can accommodate general function approximation to scale to complex environments.
  arXiv  Detail & Related papers  (2021-02-14T00:06:54Z)
• Sufficiently Accurate Model Learning for Planning [119.80502738709937]
  This paper introduces the constrained Sufficiently Accurate model learning approach. It provides examples of such problems, and presents a theorem on how close some approximate solutions can be. The approximate solution quality will depend on the function parameterization, loss and constraint function smoothness, and the number of samples in model learning.
  arXiv  Detail & Related papers  (2021-02-11T16:27:31Z)
• Model-Agnostic Explanations using Minimal Forcing Subsets [11.420687735660097]
  We propose a new model-agnostic algorithm to identify a minimal set of training samples that are indispensable for a given model's decision. Our algorithm identifies such a set of "indispensable" samples iteratively by solving a constrained optimization problem. Results show that our algorithm is an effective and easy-to-comprehend tool that helps to better understand local model behavior.
  arXiv  Detail & Related papers  (2020-11-01T22:45:16Z)
• Learning Invariances for Interpretability using Supervised VAE [0.0]
  We learn model invariances as a means of interpreting a model. We propose a supervised form of variational auto-encoders (VAEs) We show how combining our model with feature attribution methods it is possible to reach a more fine-grained understanding about the decision process of the model.
  arXiv  Detail & Related papers  (2020-07-15T10:14:16Z)
• Goal-Aware Prediction: Learning to Model What Matters [105.43098326577434]
  One of the fundamental challenges in using a learned forward dynamics model is the mismatch between the objective of the learned model and that of the downstream planner or policy. We propose to direct prediction towards task relevant information, enabling the model to be aware of the current task and encouraging it to only model relevant quantities of the state space. We find that our method more effectively models the relevant parts of the scene conditioned on the goal, and as a result outperforms standard task-agnostic dynamics models and model-free reinforcement learning.
  arXiv  Detail & Related papers  (2020-07-14T16:42:59Z)
• Control as Hybrid Inference [62.997667081978825]
  We present an implementation of CHI which naturally mediates the balance between iterative and amortised inference. We verify the scalability of our algorithm on a continuous control benchmark, demonstrating that it outperforms strong model-free and model-based baselines.
  arXiv  Detail & Related papers  (2020-07-11T19:44:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.