A popular perspective in Reinforcement learning (RL) casts the problem as probabilistic inference on a graphical model of the Markov decision process (MDP). The core object of study is the probability of each state-action pair being visited under the optimal policy.

We show that our "Joint Structure and

In addition to being a challenging problem due to the super-exponentially large search space, learning a single DAG structure from data may also lead to confident but incorrect predictions (Madigan et al.,

However, practical posteriors are often, even locally, highly non-Gaussian, and empirical performance deteriorates.

By now Bayesian methods are routinely used in practice for solving inverse problems.

A.1 Glossary The main notations in this paper are shown in Table 5. E[ D (X, y)] (15) Eq. (16) is the definition of Bregman divergence: D Hence, the selected function g should be convex. Besides, the non-maximum entropy rule approach is also worth considering, but we focus on maximum entropy prior in this section. In this section, inspired by MAML, we briefly introduce a meta-step-based implementation method. The mean of the SX-family prior in Eq. (8) is used in regular term, which can be In this work, we use the approximation Bayesian methods. The local training process based on regular terms differs from Bayesian learning based on sampling, i.e., each time a model needs to be obtained by sampling the model There are three parts in Eq. (13) we need to deal with, and the first-order methods are as shown below: In recent years, PFL has found use not only in predictive tasks like mobile device input methods but also in areas where privacy is paramount, such as healthcare and finance.