Mind the Uncertainty: Risk-Aware and Actively Exploring Model-Based Reinforcement Learning

• URL: http://arxiv.org/abs/2309.05582v1
• Date: Mon, 11 Sep 2023 16:10:58 GMT
• Title: Mind the Uncertainty: Risk-Aware and Actively Exploring Model-Based Reinforcement Learning
• Authors: Marin Vlastelica, Sebastian Blaes, Cristina Pineri, Georg Martius
• Abstract summary: We introduce a simple but effective method for managing risk in model-based reinforcement learning with trajectory sampling. Experiments indicate that the separation of uncertainties is essential to performing well with data-driven approaches in uncertain and safety-critical control environments.
• Score: 26.497229327357935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a simple but effective method for managing risk in model-based reinforcement learning with trajectory sampling that involves probabilistic safety constraints and balancing of optimism in the face of epistemic uncertainty and pessimism in the face of aleatoric uncertainty of an ensemble of stochastic neural networks.Various experiments indicate that the separation of uncertainties is essential to performing well with data-driven MPC approaches in uncertain and safety-critical control environments.

Related papers

• Uncertainty-aware Distributional Offline Reinforcement Learning [26.34178581703107]
  offline reinforcement learning (RL) presents distinct challenges as it relies solely on observational data. We propose an uncertainty-aware distributional offline RL method to simultaneously address both uncertainty and environmentality. Our method is rigorously evaluated through comprehensive experiments in both risk-sensitive and risk-neutral benchmarks, demonstrating its superior performance.
  arXiv  Detail & Related papers  (2024-03-26T12:28:04Z)
• The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness [50.52507648690234]
  Federated learning has the risk of skewing fine-tuning features and compromising the robustness of the model. We introduce three robustness indicators and conduct experiments across diverse robust datasets. Our approach markedly enhances the robustness across diverse scenarios, encompassing various parameter-efficient fine-tuning methods.
  arXiv  Detail & Related papers  (2024-01-25T09:18:51Z)
• Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling [69.83976050879318]
  In large language models (LLMs), identifying sources of uncertainty is an important step toward improving reliability, trustworthiness, and interpretability. In this paper, we introduce an uncertainty decomposition framework for LLMs, called input clarification ensembling. Our approach generates a set of clarifications for the input, feeds them into an LLM, and ensembles the corresponding predictions.
  arXiv  Detail & Related papers  (2023-11-15T05:58:35Z)
• One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning [25.218430053391884]
  We propose risk-sensitivity as a mechanism to jointly address both of these issues. Risk-aversion to aleatoric uncertainty discourages actions that may result in poor outcomes due to environmentity. Our experiments show that our algorithm achieves competitive performance on deterministic benchmarks.
  arXiv  Detail & Related papers  (2022-11-30T21:24:11Z)
• Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty [68.00748155945047]
  Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reachability. Our contribution is a novel abstraction-based controller method for continuous-state models with noise, uncertain parameters, and external disturbances.
  arXiv  Detail & Related papers  (2022-10-12T07:57:03Z)
• Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning [35.791555387656956]
  Aleatoric uncertainty results from the irreducible environment leading to inherently risky states and actions. Epistemic uncertainty results from the limited information accumulated during learning to make informed decisions. Characterizing aleatoric and uncertainty can be used to speed up learning in a training environment, improve generalization to similar testing environments, and flag unfamiliar behavior in anomalous testing environments.
  arXiv  Detail & Related papers  (2022-06-03T13:20:16Z)
• The Unreasonable Effectiveness of Deep Evidential Regression [72.30888739450343]
  A new approach with uncertainty-aware regression-based neural networks (NNs) shows promise over traditional deterministic methods and typical Bayesian NNs. We detail the theoretical shortcomings and analyze the performance on synthetic and real-world data sets, showing that Deep Evidential Regression is a quantification rather than an exact uncertainty.
  arXiv  Detail & Related papers  (2022-05-20T10:10:32Z)
• Risk Sensitive Model-Based Reinforcement Learning using Uncertainty Guided Planning [0.0]
  In this paper, risk sensitivity is promoted in a model-based reinforcement learning algorithm. We propose uncertainty guided cross-entropy method planning, which penalises action sequences that result in high variance state predictions. Experiments display the ability for the agent to identify uncertain regions of the state space during planning and to take actions that maintain the agent within high confidence areas.
  arXiv  Detail & Related papers  (2021-11-09T07:28:00Z)
• Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning [30.86992077157326]
  This paper introduces Risk Advisor, a novel post-hoc meta-learner for estimating failure risks and predictive uncertainties of any already-trained black-box classification model. In addition to providing a risk score, the Risk Advisor decomposes the uncertainty estimates into aleatoric and epistemic uncertainty components. Experiments on various families of black-box classification models and on real-world and synthetic datasets show that the Risk Advisor reliably predicts deployment-time failure risks.
  arXiv  Detail & Related papers  (2021-09-09T17:23:31Z)
• Probabilistic robust linear quadratic regulators with Gaussian processes [73.0364959221845]
  Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design. We present a novel controller synthesis for linearized GP dynamics that yields robust controllers with respect to a probabilistic stability margin.
  arXiv  Detail & Related papers  (2021-05-17T08:36:18Z)
• DEUP: Direct Epistemic Uncertainty Prediction [56.087230230128185]
  Epistemic uncertainty is part of out-of-sample prediction error due to the lack of knowledge of the learner. We propose a principled approach for directly estimating epistemic uncertainty by learning to predict generalization error and subtracting an estimate of aleatoric uncertainty.
  arXiv  Detail & Related papers  (2021-02-16T23:50:35Z)

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.