An Analysis Of Measure-valued Derivatives For Policy Gradients

Abstract

Reinforcement learning methods for robotics are increasingly successful due to the constant development of better policy gradient techniques. A precise (low variance) and accurate (low bias) gradient estimator is crucial to face increasingly complex tasks. Traditional policy gradient algorithms use the likelihood-ratio trick, which is known to produce unbiased but high variance estimates. More modern approaches exploit the reparametrization trick, which gives lower variance gradient estimates but requires differentiable value function approximators. In this work, we study a different type of stochastic gradient estimator - the Measure-Valued Derivative. This estimator is unbiased, has low variance, and can be used with differentiable and non-differentiable function approximators. We empirically evaluate this estimator in the actor-critic policy gradient setting and show that it can reach comparable performance with methods based on the likelihood-ratio or reparametrization tricks, both

Stats

• citations1
• S2 citations—
• github stars0
• HF likes0
• heat score2.26
• arxiv keycarvalho2022an

Related papers

• An Empirical Analysis Of Measure-valued Derivatives For Policy Gradients (2021)0.00
• Learning Value Functions In Deep Policy Gradients Using Residual Variance (2020)0.00
• Model-free Policy Learning With Reward Gradients (2021)0.00
• Policy Gradient Using Weak Derivatives For Reinforcement Learning (2020)0.00
• Variance Reduction For Policy-gradient Methods Via Empirical Variance Minimization (2022)0.00
• On The Model-based Stochastic Value Gradient For Continuous Reinforcement Learning (2020)0.00
• Action-depedent Control Variates For Policy Optimization Via Stein's Identity (2017)0.00
• Unifying Gradient Estimators For Meta-reinforcement Learning Via Off-policy Evaluation (2021)0.00