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...
离线演员-评论员算法(Off-Policy Actor-Critic) 三、确定性策略梯度算法(Deterministic Policy Gradient Algorithms) 一些基本的符号定义: 确定性策略梯度理论(Deterministic Policy Gradient Theorem) 确定性演员-评论员算法(Deterministic Actor-Critic Algorithms) 同策略演员-评论员算法(On-Policy Deterministic Actor-Critic...
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 the efficacy of our method both mathematically ...
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...
1. Policy Gradient (PG)方法的优点: 相对于一般的 Value Based 方法(如估计Q(s,a)值), PG更加适合运用在连续的或者较大的Action Space(实际的机器人控制等等),因为随着 Action Space的增大,Q(s,a)的规模也会相对增大,对具体的实现造成很大的困难(如DQN的输出与Action的个数有关)。而对PG来说这种问题.....
Deterministic Policy Gradient Algorithms, Silver et al. 2014 Continuous Control With Deep Reinforcement Learning, Lillicrap et al. 2016 Why These Papers? Silver 2014 is included because it establishes the theory underlying deterministic policy gradients (DPG). Lillicrap 2016 is included because it adap...
Deterministic Policy Gradient, or DPG, is a policy gradient method for reinforcement learning. Instead of the policy function $\pi\left(.\mid{s}\right)$ being modeled as a probability distribution, DPG considers and calculates gradients for a deterministic policy $a = \mu_{theta}\left(s\righ...
Policy-Gradient Methods Policy-Gradient (PG) algorithms optimize a policy end-to-end by computing noisy estimates of the gradient of the expected reward of the policy and then updating the policy in the gradient direction. Traditionally, PG methods have assumed a stochastic policyμ(a|s)μ(a|...
这是一篇值得细读的论文:Deterministic Policy Gradient Algorithms Stochastic Policy Gradient (SPG) 是通过参数化的概率分布 πθ(a|s)=P[a|s;θ] 随机地选择动作,即 πθ(a|s) 是一个动作的概率分布。 Deterministic Policy Gradient (DPG) 与SPG 不同之处是,这个方法会确定地选择一个动作:a=μθ(s)。
[15]Deterministic Policy Gradient Algorithms, Silver et al, 2014.Algorithm: DPG. 背景介绍 Deterministic Policy是相对于Stochastic Policy而言的。其中Stochastic Policy的表达式为 πθ(a|s)=P[a|s;θ] ,在实际应用中,大家往往采用高斯分布来作为策略的分布。其中高斯分布的均值和方差都由神经网络来近似。 而...