本文是OpenAI强化学习介绍Spinning up的第四篇,来自Deepmind。 确定性的策略梯度就是:动作-值函数 V 的期望梯度显然,满足确定性,计算量肯定更小,所以本文的核心就是—— 它为什么有效。 不多说,直接上推导。…
DPG是一种确定性策略梯度算法,是比较早发出来的确定性算法,也是DDPG的基础。 1. 研究背景 随机策略梯度的局限性:在连续动作空间中,随机策略梯度(如 REINFORCE)需对动作空间积分,导致高方差和计算低效,尤其在高维动作空间中表现不佳。 确定性策略的潜力:直接优化确定性策略(如控制领域的微分控制器)可避免积分,但传统...
该证明与策略梯度函数逼近论文Policy Gradient Methods for Reinforcement Learning with Function Approximation证明类似,表明了目标函数梯度不涉及平稳分布函数导数的计算。具体的定理形式为: 作者首先给出了On policy的DPG算法: δt=rt+γQw(st+1,at+1)−Qw(st,at)wt+1=wt+αwδt∇wQw(st,at)θt+1=θt...
We present two example actor–critic algorithms. Both algorithms employ our developed policy gradient theorem for their actors, but use two different critics; one uses a simple SARSA update while the other one uses the same on‐policy update but with compatible function approximators. We demonstrate...
We demonstratethat deterministic policy gradient algorithms cansignificantly outperform their stochastic counter-parts in high-dimensional action spaces.1. IntroductionPolicy gradient algorithms are widely used in reinforce-mentlearningproblemswithcontinuousactionspaces. Thebasic idea is to represent the policy ...
In these cases, the stochastic policy gradient is inapplicable,whereas our methods may still be useful.Deterministic Policy Gradient Algorithms 2.Background 2.1.Preliminaries We study reinforcement learning and control problems in which an agent acts in a stochastic environment by sequen-tially ...
在这一部分作者证明来deterministic policy gradient 是stochastic policy gradient的极限情况。有了deterministic policy gradient theorem，接下来推导on-policy off-policy actor-critic algorithms。Performance objective of target policy, averaged over the state distribution of the behavior policy 求导 ...
Deterministic Policy Gradient Algorithms David Silver DeepMind Technologies, London, UK Guy Lever University College London, UK Nicolas Heess, Thomas Degris, Daan Wierstra, Martin Riedmiller DeepMind Technologies, London, UK DAVID @ DEEPMIND . COM GUY. LEVER @ UCL . AC . UK *@ DEEPMIND . COM...
running the trained policy with the test_policy.py tool, or loading the whole saved graph into a program with restore_tf_graph.References Relevant Papers Deterministic Policy Gradient Algorithms, Silver et al. 2014 Continuous Control With Deep Reinforcement Learning, Lillicrap et al. 2016 Why These...
Off-Policy Correction for Deep Deterministic Policy Gradient Algorithms via Batch Prioritized Experience Replay The experience replay mechanism allows agents to use the experiences multiple times. In prior works, the sampling probability of the transitions was adjusted according to their importance. ...