How Does Return Distribution in Distributional Reinforcement Learning Help Optimization?

• URL: http://arxiv.org/abs/2209.14513v2
• Date: Mon, 23 Sep 2024 00:19:05 GMT
• Title: How Does Return Distribution in Distributional Reinforcement Learning Help Optimization?
• Authors: Ke Sun, Bei Jiang, Linglong Kong,
• Abstract summary: We investigate the optimization advantages of distributional RL within the Neural Fitted Z-Iteration(Neural FZI) framework. We show that distributional RL has desirable smoothness characteristics and hence enjoys stable gradients. Our research findings illuminate how the return distribution in distributional RL algorithms helps the optimization.
• Score: 10.149055921090572
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distributional reinforcement learning, which focuses on learning the entire return distribution instead of only its expectation in standard RL, has demonstrated remarkable success in enhancing performance. Despite these advancements, our comprehension of how the return distribution within distributional RL still remains limited. In this study, we investigate the optimization advantages of distributional RL by utilizing its extra return distribution knowledge over classical RL within the Neural Fitted Z-Iteration~(Neural FZI) framework. To begin with, we demonstrate that the distribution loss of distributional RL has desirable smoothness characteristics and hence enjoys stable gradients, which is in line with its tendency to promote optimization stability. Furthermore, the acceleration effect of distributional RL is revealed by decomposing the return distribution. It shows that distributional RL can perform favorably if the return distribution approximation is appropriate, measured by the variance of gradient estimates in each environment. Rigorous experiments validate the stable optimization behaviors of distributional RL and its acceleration effects compared to classical RL. Our research findings illuminate how the return distribution in distributional RL algorithms helps the optimization.

Related papers

• The Power of Perturbation under Sampling in Solving Extensive-Form Games [56.013335390600524]
  This paper investigates how perturbation does and does not improve the Follow-the-Regularized-Leader (FTRL) algorithm in imperfect-information extensive-form games. Perturbing the expected payoffs guarantees that the FTRL dynamics reach an approximate equilibrium. We show that in the last-iterate sense, the FTRL consistently outperforms the non-samplinged FTRL.
  arXiv  Detail & Related papers  (2025-01-28T00:29:38Z)
• Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning [30.64409258999151]
  We show that action-conditioned return distributions collapse to their underlying policy's return distribution as the decision frequency increases. We also introduce the superiority as a probabilistic generalization of the advantage. Through simulations in an option-trading domain, we validate that proper modeling of the superiority distribution produces improved controllers at high decision frequencies.
  arXiv  Detail & Related papers  (2024-10-14T19:18:38Z)
• More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning [58.626683114119906]
  We show that Distributional Reinforcement Learning (DistRL) can obtain second-order bounds in both online and offline RL. Our results are the first second-order bounds for low-rank MDPs and for offline RL.
  arXiv  Detail & Related papers  (2024-02-11T13:25:53Z)
• Distributional Reinforcement Learning with Dual Expectile-Quantile Regression [51.87411935256015]
  quantile regression approach to distributional RL provides flexible and effective way of learning arbitrary return distributions. We show that distributional guarantees vanish, and we empirically observe that the estimated distribution rapidly collapses to its mean estimation. Motivated by the efficiency of $L$-based learning, we propose to jointly learn expectiles and quantiles of the return distribution in a way that allows efficient learning while keeping an estimate of the full distribution of returns.
  arXiv  Detail & Related papers  (2023-05-26T12:30:05Z)
• One-Step Distributional Reinforcement Learning [10.64435582017292]
  We present the simpler one-step distributional reinforcement learning (OS-DistrRL) framework. We show that our approach comes with a unified theory for both policy evaluation and control. We propose two OS-DistrRL algorithms for which we provide an almost sure convergence analysis.
  arXiv  Detail & Related papers  (2023-04-27T06:57:00Z)
• Policy Evaluation in Distributional LQR [70.63903506291383]
  We provide a closed-form expression of the distribution of the random return. We show that this distribution can be approximated by a finite number of random variables. Using the approximate return distribution, we propose a zeroth-order policy gradient algorithm for risk-averse LQR.
  arXiv  Detail & Related papers  (2023-03-23T20:27:40Z)
• Distributional Reinforcement Learning for Multi-Dimensional Reward Functions [91.88969237680669]
  We introduce Multi-Dimensional Distributional DQN (MD3QN) to model the joint return distribution from multiple reward sources. As a by-product of joint distribution modeling, MD3QN can capture the randomness in returns for each source of reward. In experiments, our method accurately models the joint return distribution in environments with richly correlated reward functions.
  arXiv  Detail & Related papers  (2021-10-26T11:24:23Z)
• The Benefits of Being Categorical Distributional: Uncertainty-aware Regularized Exploration in Reinforcement Learning [17.64056793687686]
  We find potential superiority of distributional RL can be attributed to a derived distribution-matching entropy regularization. Our study offers a new perspective from the exploration to explain the intrinsic benefits of adopting distributional learning in RL.
  arXiv  Detail & Related papers  (2021-10-07T03:14:46Z)
• Bayesian Distributional Policy Gradients [2.28438857884398]
  Distributional Reinforcement Learning maintains the entire probability distribution of the reward-to-go, i.e. the return. Bayesian Distributional Policy Gradients (BDPG) uses adversarial training in joint-contrastive learning to estimate a variational posterior from the returns.
  arXiv  Detail & Related papers  (2021-03-20T23:42:50Z)
• Instabilities of Offline RL with Pre-Trained Neural Representation [127.89397629569808]
  In offline reinforcement learning (RL), we seek to utilize offline data to evaluate (or learn) policies in scenarios where the data are collected from a distribution that substantially differs from that of the target policy to be evaluated. Recent theoretical advances have shown that such sample-efficient offline RL is indeed possible provided certain strong representational conditions hold. This work studies these issues from an empirical perspective to gauge how stable offline RL methods are.
  arXiv  Detail & Related papers  (2021-03-08T18:06:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.