Learning Utilities from Demonstrations in Markov Decision Processes

• URL: http://arxiv.org/abs/2409.17355v1
• Date: Wed, 25 Sep 2024 21:01:15 GMT
• Title: Learning Utilities from Demonstrations in Markov Decision Processes
• Authors: Filippo Lazzati, Alberto Maria Metelli,
• Abstract summary: We propose a novel model of behavior in Markov Decision Processes (MDPs) that explicitly represents the agent's risk attitude through a utility function. We then define the Utility Learning problem as the task of inferring the observed agent's risk attitude, encoded via a utility function, from demonstrations in MDPs. We devise two provably efficient algorithms for UL in a finite-data regime, and we analyze their sample complexity.
• Score: 18.205765143671858
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Our goal is to extract useful knowledge from demonstrations of behavior in sequential decision-making problems. Although it is well-known that humans commonly engage in risk-sensitive behaviors in the presence of stochasticity, most Inverse Reinforcement Learning (IRL) models assume a risk-neutral agent. Beyond introducing model misspecification, these models do not directly capture the risk attitude of the observed agent, which can be crucial in many applications. In this paper, we propose a novel model of behavior in Markov Decision Processes (MDPs) that explicitly represents the agent's risk attitude through a utility function. We then define the Utility Learning (UL) problem as the task of inferring the observed agent's risk attitude, encoded via a utility function, from demonstrations in MDPs, and we analyze the partial identifiability of the agent's utility. Furthermore, we devise two provably efficient algorithms for UL in a finite-data regime, and we analyze their sample complexity. We conclude with proof-of-concept experiments that empirically validate both our model and our algorithms.

Related papers

• On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning [85.75164588939185]
  We study the discriminative probabilistic modeling problem on a continuous domain for (multimodal) self-supervised representation learning. We conduct generalization error analysis to reveal the limitation of current InfoNCE-based contrastive loss for self-supervised representation learning.
  arXiv  Detail & Related papers  (2024-10-11T18:02:46Z)
• R-AIF: Solving Sparse-Reward Robotic Tasks from Pixels with Active Inference and World Models [50.19174067263255]
  We introduce prior preference learning techniques and self-revision schedules to help the agent excel in sparse-reward, continuous action, goal-based robotic control POMDP environments. We show that our agents offer improved performance over state-of-the-art models in terms of cumulative rewards, relative stability, and success rate.
  arXiv  Detail & Related papers  (2024-09-21T18:32:44Z)
• Inverse-RLignment: Inverse Reinforcement Learning from Demonstrations for LLM Alignment [62.05713042908654]
  We introduce Alignment from Demonstrations (AfD), a novel approach leveraging high-quality demonstration data to overcome these challenges. We formalize AfD within a sequential decision-making framework, highlighting its unique challenge of missing reward signals. Practically, we propose a computationally efficient algorithm that extrapolates over a tailored reward model for AfD.
  arXiv  Detail & Related papers  (2024-05-24T15:13:53Z)
• Zero-shot Imitation Policy via Search in Demonstration Dataset [0.16817021284806563]
  Behavioral cloning uses a dataset of demonstrations to learn a policy. We propose to use latent spaces of pre-trained foundation models to index a demonstration dataset. Our approach can effectively recover meaningful demonstrations and show human-like behavior of an agent in the Minecraft environment.
  arXiv  Detail & Related papers  (2024-01-29T18:38:29Z)
• Modeling Boundedly Rational Agents with Latent Inference Budgets [56.24971011281947]
  We introduce a latent inference budget model (L-IBM) that models agents' computational constraints explicitly. L-IBMs make it possible to learn agent models using data from diverse populations of suboptimal actors. We show that L-IBMs match or outperform Boltzmann models of decision-making under uncertainty.
  arXiv  Detail & Related papers  (2023-12-07T03:55:51Z)
• Risk-Aware Distributed Multi-Agent Reinforcement Learning [8.287693091673658]
  We develop a distributed MARL approach to solve decision-making problems in unknown environments by learning risk-aware actions. We then propose a distributed MARL algorithm called the CVaR QD-Learning algorithm, and establish that value functions of individual agents reaches consensus.
  arXiv  Detail & Related papers  (2023-04-04T17:56:44Z)
• Deceptive Decision-Making Under Uncertainty [25.197098169762356]
  We study the design of autonomous agents that are capable of deceiving outside observers about their intentions while carrying out tasks. By modeling the agent's behavior as a Markov decision process, we consider a setting where the agent aims to reach one of multiple potential goals. We propose a novel approach to model observer predictions based on the principle of maximum entropy and to efficiently generate deceptive strategies.
  arXiv  Detail & Related papers  (2021-09-14T14:56:23Z)
• Feature-Based Interpretable Reinforcement Learning based on State-Transition Models [3.883460584034766]
  Growing concerns regarding the operational usage of AI models in the real-world has caused a surge of interest in explaining AI models' decisions to humans. We propose a method for offering local explanations on risk in reinforcement learning.
  arXiv  Detail & Related papers  (2021-05-14T23:43:11Z)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)
• SAMBA: Safe Model-Based & Active Reinforcement Learning [59.01424351231993]
  SAMBA is a framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations. We provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.
  arXiv  Detail & Related papers  (2020-06-12T10:40:46Z)

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.