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。…
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,但传统方法依赖模型或被认为梯度不存在。 2. 核心贡献 确定性策略梯度定理 证明了确定性策略梯度的存在性,其形式为动作值函数梯度的期望:\ \(\nabla_{\theta} J(\mu_{\theta}) = \mathbb{E}_{s \sim \rho^{\mu}}\left[\nabl...
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...
COMDeepMind Technologies, London, UKAbstractIn this paper we consider deterministic policygradient algorithms for reinforcement learningwith continuous actions. The deterministic pol-icy gradient has a particularly appealing form: itis the expected gradient of the action-value func-tion. This simple form m...
Policygradientalgorithmsarewidelyusedinreinforce-mentlearningproblemswithcontinuousactionspaces.Thebasicideaistorepresentthepolicybyaparametricprob-abilitydistributionπθ(a|s)=P[a|s;θ]thatstochasticallyselectsactionainstatesaccordingtoparametervectorθ.Policygradientalgorithmstypicallyproceedbysamplingthisstochasticpolic...
所以在2014年,由Deepmind的D.Silver等提出 Deterministic policy gradient algorithms,即確定性策略演算法。 一定的策略μ \muμ,對應一定的確定性動作的策略網路的引數θ μ \theta^{\mu }θμ,在同一個狀態s ss處,,動作a t a_tat是唯一確定的, ...
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...
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...