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A Parametric Class of Approximate Gradient Updates for Policy Optimization
Approaches to policy optimization have been motivated from diverse principles, based on how the parametric model is interpreted (e. g. value versus policy representation) or how the learning objective is formulated, yet they share a common goal of maximizing expected return.
Understanding and Leveraging Overparameterization in Recursive Value Estimation
To better understand the utility of deep models in RL we present an analysis of recursive value estimation using overparameterized linear representations that provides useful, transferable findings.
Characterizing the Gap Between Actor-Critic and Policy Gradient
Actor-critic (AC) methods are ubiquitous in reinforcement learning.
Surrogate Objectives for Batch Policy Optimization in One-step Decision Making
We investigate batch policy optimization for cost-sensitive classification and contextual bandits---two related tasks that obviate exploration but require generalizing from observed rewards to action selections in unseen contexts.
Variational Rejection Sampling
Learning latent variable models with stochastic variational inference is challenging when the approximate posterior is far from the true posterior, due to high variance in the gradient estimates.
