• Corpus ID: 195346668

Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems

@article{Depeweg2017DecompositionOU,
  title={Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems},
  author={Stefan Depeweg and Jos{\'e} Miguel Hern{\'a}ndez-Lobato and Finale Doshi-Velez and Steffen Udluft},
  journal={ArXiv},
  year={2017},
  volume={abs/1710.07283}
}
Bayesian neural networks (BNNs) with latent variables are probabilistic models which can automatically identify complex stochastic patterns in the data. We study in these models a decomposition of predictive uncertainty into its epistemic and aleatoric components. We show how such a decomposition arises naturally in a Bayesian active learning scenario and develop a new objective for reliable reinforcement learning (RL) with an epistemic and aleatoric risk element. Our experiments illustrate the… 

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References

SHOWING 1-10 OF 32 REFERENCES

Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks

TLDR
An algorithm for model-based reinforcement learning that combines Bayesian neural networks (BNNs) with random roll-outs and stochastic optimization for policy learning and achieves promising results in a real-world scenario for controlling a gas turbine.

Model based Bayesian Exploration

TLDR
This paper explicitly represents uncertainty about the parameters of the model and build probability distributions over Q-values based on these that are used to compute a myopic approximation to the value of information for each action and hence to select the action that best balances exploration and exploitation.

Efficient Uncertainty Propagation for Reinforcement Learning with Limited Data

TLDR
This paper presents a method to incorporate the estimator's uncertainties and propagate them to the conclusions by being only approximate, which considerably increases the robustness of the derived policies compared to the standard approach.

Weight Uncertainty in Neural Networks

TLDR
This work introduces a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop, and shows how the learnt uncertainty in the weights can be used to improve generalisation in non-linear regression problems.

Improving PILCO with Bayesian Neural Network Dynamics Models

TLDR
PILCO’s framework is extended to use Bayesian deep dynamics models with approximate variational inference, allowing PILCO to scale linearly with number of trials and observation space dimensionality, and it is shown that moment matching is a crucial simplifying assumption made by the model.

Risk-Sensitive Reinforcement Learning

TLDR
A risk-sensitive Q-learning algorithm is derived, which is necessary for modeling human behavior when transition probabilities are unknown, and applied to quantify human behavior in a sequential investment task and is found to provide a significantly better fit to the behavioral data and leads to an interpretation of the subject's responses that is indeed consistent with prospect theory.

Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning

TLDR
A robust method to learn multimodal transitions using function approximation, which is a key preliminary for model-based RL in stochastic domains, is shown.

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

TLDR
This work presents a novel scalable method for learning Bayesian neural networks, called probabilistic backpropagation (PBP), which works by computing a forward propagation of probabilities through the network and then doing a backward computation of gradients.

Bayesian learning for data-efficient control

TLDR
This thesis uses probabilistic Bayesian modelling to learn systems from scratch, similar to the PILCO algorithm, and takes a step towards data efficient learning of high-dimensional control using Bayesian neural networks (BNN).

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

TLDR
A Bayesian deep learning framework combining input-dependent aleatoric uncertainty together with epistemic uncertainty is presented, which makes the loss more robust to noisy data, also giving new state-of-the-art results on segmentation and depth regression benchmarks.