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Found 11 papers, 5 papers with code
Function-Space Regularization for Deep Bayesian Classification
Bayesian deep learning approaches assume model parameters to be latent random variables and infer posterior distributions to quantify uncertainty, increase safety and trust, and prevent overconfident and unpredictable behavior.
Coherent Soft Imitation Learning
Imitation learning methods seek to learn from an expert either through behavioral cloning (BC) of the policy or inverse reinforcement learning (IRL) of the reward.
Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes
Monte Carlo methods have become increasingly relevant for control of non-differentiable systems, approximate dynamics models and learning from data.
Function-Space Variational Inference for Deep Bayesian Classification
Bayesian deep learning approaches assume model parameters to be latent random variables and infer posterior predictive distributions to quantify uncertainty, increase safety and trust, and prevent overconfident and unpredictable behavior.
Efficient Stochastic Optimal Control through Approximate Bayesian Input Inference
Optimal control under uncertainty is a prevailing challenge for many reasons.
Advancing Trajectory Optimization with Approximate Inference: Exploration, Covariance Control and Adaptive Risk
Discrete-time stochastic optimal control remains a challenging problem for general, nonlinear systems under significant uncertainty, with practical solvers typically relying on the certainty equivalence assumption, replanning and/or extensive regularization.
Neural Linear Models with Functional Gaussian Process Priors
Neural linear models (NLM) and Gaussian processes (GP) are both examples of Bayesian linear regression on rich feature spaces.
Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning
A limitation of model-based reinforcement learning (MBRL) is the exploitation of errors in the learned models.
Model-based Reinforcement Learning
reinforcement-learning
+1
A Differentiable Newton Euler Algorithm for Multi-body Model Learning
In this work, we examine a spectrum of hybrid model for the domain of multi-body robot dynamics.
Active Inference or Control as Inference? A Unifying View
Active inference (AI) is a persuasive theoretical framework from computational neuroscience that seeks to describe action and perception as inference-based computation.
Stochastic Optimal Control as Approximate Input Inference
Optimal control of stochastic nonlinear dynamical systems is a major challenge in the domain of robot learning.
