强化学习读书笔记 - 13 - 策略梯度方法(Policy Gradient Methods)

强化学习读书笔记 - 13 - 策略梯度方法(Policy Gradient Methods)

学习笔记： Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto c 2014, 2015, 2016

参照

• Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto c 2014, 2015, 2016
• 强化学习读书笔记 - 00 - 术语和数学符号
• 强化学习读书笔记 - 01 - 强化学习的问题
• 强化学习读书笔记 - 02 - 多臂老O虎O机问题
• 强化学习读书笔记 - 03 - 有限马尔科夫决策过程
• 强化学习读书笔记 - 04 - 动态规划
• 强化学习读书笔记 - 05 - 蒙特卡洛方法(Monte Carlo Methods)
• 强化学习读书笔记 - 06~07 - 时序差分学习(Temporal-Difference Learning)
• 强化学习读书笔记 - 08 - 规划式方法和学习式方法
• 强化学习读书笔记 - 09 - on-policy预测的近似方法
• 强化学习读书笔记 - 10 - on-policy控制的近似方法
• 强化学习读书笔记 - 11 - off-policy的近似方法
• 强化学习读书笔记 - 12 - 资格痕迹(Eligibility Traces)

需要了解强化学习的数学符号，先看看这里：

• 强化学习读书笔记 - 00 - 术语和数学符号

策略梯度方法(Policy Gradient Methods)

基于价值函数的思路

\text{Reinforcement Learning} \doteq \pi_* \\ \quad \updownarrow \\ \pi_* \doteq \{ \pi(s) \}, \ s \in \mathcal{S} \\ \quad \updownarrow \\ \begin{cases} \pi(s) = \underset{a}{argmax} \ v_{\pi}(s' | s, a), \ s' \in S(s), \quad \text{or} \\ \pi(s) = \underset{a}{argmax} \ q_{\pi}(s, a) \\ \end{cases} \\ \quad \updownarrow \\ \begin{cases} v_*(s), \quad \text{or} \\ q_*(s, a) \\ \end{cases} \\ \quad \updownarrow \\ \text{approximation cases:} \\ \begin{cases} \hat{v}(s, \theta) \doteq \theta^T \phi(s), \quad \text{state value function} \\ \hat{q}(s, a, \theta) \doteq \theta^T \phi(s, a), \quad \text{action value function} \\ \end{cases} \\ where \\ \theta \text{ - value function's weight vector} \\

策略梯度方法的新思路(Policy Gradient Methods)

策略梯度定理（The policy gradient theorem）

情节性任务

如何计算策略的价值\eta

\eta(\theta) \doteq v_{\pi_\theta}(s_0) \\ where \\ \eta \text{ - the performance measure} \\ v_{\pi_\theta} \text{ - the true value function for } \pi_\theta \text{, the policy determined by } \theta \\ s_0 \text{ - some particular state} \\

• 策略梯度定理 \nabla \eta(\theta) = \sum_s d_{\pi}(s) \sum_{a} q_{\pi}(s,a) \nabla_\theta \pi(a|s, \theta) \\ where \\ d(s) \text{ - on-policy distribution, the fraction of time spent in s under the target policy } \pi \\ \sum_s d(s) = 1 \\

蒙特卡洛策略梯度强化算法(ERINFORCE: Monte Carlo Policy Gradient)

• 策略价值计算公式 \begin{align} \nabla \eta(\theta) & = \sum_s d_{\pi}(s) \sum_{a} q_{\pi}(s,a) \nabla_\theta \pi(a|s, \theta) \\ & = \mathbb{E}_\pi \left [ \gamma^t \sum_a q_\pi(S_t,a) \nabla_\theta \pi(a|s, \theta) \right ] \\ & = \mathbb{E}_\pi \left [ \gamma^t G_t \frac{\nabla_\theta \pi(A_t|S_t, \theta)}{\pi(A_t|S_t, \theta)} \right ] \end{align}
• Update Rule公式 \begin{align} \theta_{t+1} & \doteq \theta_t + \alpha \gamma^t G_t \frac{\nabla_\theta \pi(A_t|S_t, \theta)}{\pi(A_t|S_t, \theta)} \\ & = \theta_t + \alpha \gamma^t G_t \nabla_\theta \log \pi(A_t|S_t, \theta) \\ \end{align}
• 算法描述(ERINFORCE: A Monte Carlo Policy Gradient Method (episodic)) 请看原书，在此不做拗述。

带基数的蒙特卡洛策略梯度强化算法(ERINFORCE with baseline)

• 策略价值计算公式 \begin{align} \nabla \eta(\theta) & = \sum_s d_{\pi}(s) \sum_{a} q_{\pi}(s,a) \nabla_\theta \pi(a|s, \theta) \\ & = \sum_s d_{\pi}(s) \sum_{a} \left ( q_{\pi}(s,a) - b(s)\right ) \nabla_\theta \pi(a|s, \theta) \\ \end{align} \\ \because \\ \sum_{a} b(s) \nabla_\theta \pi(a|s, \theta) \\ \quad = b(s) \nabla_\theta \sum_{a} \pi(a|s, \theta) \\ \quad = b(s) \nabla_\theta 1 \\ \quad = 0 \\ where \\ b(s) \text{ - an arbitrary baseline function, e.g. } b(s) = \hat{v}(s, w) \\
• Update Rule公式 \delta = G_t - \hat{v}(s, w) \\ w_{t+1} = w_{t} + \beta \delta \nabla_w \hat{v}(s, w) \\ \theta_{t+1} = \theta_t + \alpha \gamma^t \delta \nabla_\theta \log \pi(A_t|S_t, \theta) \\
• 算法描述 请看原书，在此不做拗述。

角色评论算法(Actor-Critic Methods)

这个算法实际上是：

1. 带基数的蒙特卡洛策略梯度强化算法的TD通用化。
2. 加上资格迹(eligibility traces)

注：蒙特卡洛方法要求必须完成当前的情节。这样才能计算正确的回报G_t。 TD避免了这个条件（从而提高了效率），可以通过临时差分计算一个近似的回报G_t^{(0)} \approx G_t（当然也产生了不精确性）。 资格迹(eligibility traces)优化了(计算权重变量的)价值函数的微分值，e_t \doteq \nabla \hat{v}(S_t, \theta_t) + \gamma \lambda \ e_{t-1}。

• Update Rule公式 \delta = G_t^{(1)} - \hat{v}(S_t, w) \\ \quad = R_{t+1} + \gamma \hat{v}(S_{t+1}, w) - \hat{v}(S_t, w) \\ w_{t+1} = w_{t} + \beta \delta \nabla_w \hat{v}(s, w) \\ \theta_{t+1} = \theta_t + \alpha \gamma^t \delta \nabla_\theta \log \pi(A_t|S_t, \theta) \\
• Update Rule with eligibility traces公式 \delta = R + \gamma \hat{v}(s', w) - \hat{v}(s', w) \\ e^w = \lambda^w e^w + \gamma^t \nabla_w \hat{v}(s, w) \\ w_{t+1} = w_{t} + \beta \delta e_w \\ e^{\theta} = \lambda^{\theta} e^{\theta} + \gamma^t \nabla_\theta \log \pi(A_t|S_t, \theta) \\ \theta_{t+1} = \theta_t + \alpha \delta e^{\theta} \\ where \\ R + \gamma \hat{v}(s', w) = G_t^{(0)} \\ \delta \text{ - TD error} \\ e^w \text{ - eligibility trace of state value function} \\ e^{\theta} \text{ - eligibility trace of policy value function} \\
• 算法描述 请看原书，在此不做拗述。

针对连续性任务的策略梯度算法(Policy Gradient for Continuing Problems(Average Reward Rate))

• 策略价值计算公式 对于连续性任务的策略价值是每个步骤的平均奖赏。 \begin{align} \eta(\theta) \doteq r(\theta) & \doteq \lim_{n \to \infty} \frac{1}{n} \sum_{t=1}^n \mathbb{E} [R_t|\theta_0=\theta_1=\dots=\theta_{t-1}=\theta] \\ & = \lim_{t \to \infty} \mathbb{E} [R_t|\theta_0=\theta_1=\dots=\theta_{t-1}=\theta] \\ \end{align}
• Update Rule公式 \delta = G_t^{(1)} - \hat{v}(S_t, w) \\ \quad = R_{t+1} + \gamma \hat{v}(S_{t+1}, w) - \hat{v}(S_t, w) \\ w_{t+1} = w_{t} + \beta \delta \nabla_w \hat{v}(s, w) \\ \theta_{t+1} = \theta_t + \alpha \gamma^t \delta \nabla_\theta \log \pi(A_t|S_t, \theta) \\
• Update Rule Actor-Critic with eligibility traces (continuing) 公式 \delta = R - \bar{R} + \gamma \hat{v}(s', w) - \hat{v}(s', w) \\ \bar{R} = \bar{R} + \eta \delta \\ e^w = \lambda^w e^w + \gamma^t \nabla_w \hat{v}(s, w) \\ w_{t+1} = w_{t} + \beta \delta e_w \\ e^{\theta} = \lambda^{\theta} e^{\theta} + \gamma^t \nabla_\theta \log \pi(A_t|S_t, \theta) \\ \theta_{t+1} = \theta_t + \alpha \delta e^{\theta} \\ where \\ R + \gamma \hat{v}(s', w) = G_t^{(0)} \\ \delta \text{ - TD error} \\ e^w \text{ - eligibility trace of state value function} \\ e^{\theta} \text{ - eligibility trace of policy value function} \\
• 算法描述(Actor-Critic with eligibility traces (continuing)) 请看原书，在此不做拗述。 原书还没有完成，这章先停在这里