Variational Inference MPC for Bayesian Model-based Reinforcement Learning.
Masashi OkadaTadahiro TaniguchiPublished in: CoRL (2019)
Keyphrases
- variational inference
- model based reinforcement learning
- bayesian inference
- posterior distribution
- markov decision processes
- topic models
- gaussian process
- probabilistic model
- variational methods
- latent dirichlet allocation
- probabilistic graphical models
- mixture model
- latent variables
- closed form
- exact inference
- exponential family
- probability distribution
- parameter estimation
- hyperparameters
- gaussian processes
- graphical models
- posterior probability
- markov chain monte carlo
- reinforcement learning
- maximum a posteriori
- factor graphs
- bayesian framework
- hidden variables
- generative model
- text mining
- state space
- prior information
- optimal policy
- approximate inference
- em algorithm
- particle filter
- maximum likelihood
- knowledge representation
- bayesian networks