Beyond variance reduction: Understanding the true impact of baselines on policy optimization

Abstract

Bandit and reinforcement learning (RL) problems can often be framed as optimization problems where the goal is to maximize average performance while having access only to stochastic estimates of the true gradient. Traditionally, stochastic optimization theory predicts that learning dynamics are governed by the curvature of the loss function and the noise of the gradient estimates. In this paper we demonstrate that this is not the case for bandit and RL problems. To allow our analysis to be interpreted in light of multi-step MDPs, we focus on techniques derived from stochastic optimization principles (e.g., natural policy gradient and EXP3) and we show that some standard assumptions from optimization theory are violated in these problems. We present theoretical results showing that, at least for bandit problems, curvature and noise are not sufficient to explain the learning dynamics and that seemingly innocuous choices like the baseline can determine whether an algorithm converges. These theoretical findings match our empirical evaluation, which we extend to multi-state MDPs.

References in corpus (9)

• Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
• Backpropagation through the Void: Optimizing control variates for black-box gradient estimation
• Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines
• On the Global Convergence Rates of Softmax Policy Gradient Methods
• Ray Interference: a Source of Plateaus in Deep Reinforcement Learning
• PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning
• Action-depedent Control Variates for Policy Optimization via Stein's Identity
• Data-Efficient Policy Evaluation Through Behavior Policy Search
• A unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme

Cited by in corpus (4)

• On Proximal Policy Optimization's Heavy-tailed Gradients
• Understanding the Effect of Stochasticity in Policy Optimization
• Coordinate-wise Control Variates for Deep Policy Gradients
• Low Variance Trust Region Optimization with Independent Actors and Sequential Updates in Cooperative Multi-agent Reinforcement Learning