We consider reinforcement learning as solving a Markov decision process with unknown transition distribution. Based on interaction with the environment, ...
The resulting algorithm is formally intractable and we discuss two approximate solution methods, Variational Bayes and Ex- pectation Propagation. 1 Introduction.
Oct 30, 2023 · We propose a novel approach to unsupervised skill discovery based on information theory, called Value Uncertainty Variational Curriculum (VUVC).
Mar 24, 2023 · In this talk, we discuss two classes of variational methods that can be used to obtain approximate solutions with accompanying error guarantees.
In this paper, we consider policy optimization in Markov Decision Problems, where the objective is a general utility function of the state-action occupancy ...
Variational methods for Reinforcement Learning ... We consider reinforcement learning as solving a Markov decision process with unknown transition distribution.
Apr 14, 2024 · Variational methods or principles are techniques that optimize over a space of functions. It is analogous to ordinary optimization problem.
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Sep 9, 2024 · In this study, we leverage the powerful and flexible Reinforcement Learning paradigm to train an agent capable of autonomously generating quantum circuits.
Figure 1 from Variational methods for Reinforcement Learning
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This paper suggests two reinforcement learning methods, ie, a model‐based and a model free algorithm that bound the loss in relative entropy while maximizing ...
We derive a Variational Policy Gradient Theorem for RL with general utilities which establishes that the parameterized policy gradient is the solution to a ...