Mathematical Foundations of Reinforcement Learning

Basic Concepts

1. State: The status of the agent with respect to the environment.
   • State Space: The set of all states.
   • \(\mathcal{S} = \{s_i\}_{i=1}^{n}\)
2. Action: All posible actions for each state.
   • Action Space: The set of all possible actions of a state.
   • \(\mathcal{A}(s_i) = \{a_i\}_{i=1}^{n}\)
3. State Transition: When taking an action, the agent may move from one state to another.
   • \(s_1 \xrightarrow{a_1} s_2\)
   • Defines the interaction with the environment.
4. Policy: Tells the agent what actions to take at a state.
   • Mathematical representation: conditional probability
5. Reward: A real number the agent get after taking an action.
   • Depends on the current state and action, not the next one.
6. Trajectory: A state-action-reward chain.
   • \(s_1 \xrightarrow[r=0]{a_1} s_2 \xrightarrow[r=0]{a_2} s_3 \xrightarrow[r=0]{a_3} s_4\)
7. Return: The sum of all the rewards collected along a trajectory.
8. Discount rate: $$ \gamma \in [0, 1] $$
   • \(R_{discounted} = \gamma^0 r_1 + \gamma^1 r_2 + \dots\)
   • Balance the far \((\gamma \rightarrow 1)\) and near \((\gamma \rightarrow 0)\) future rewards.
9. Episode/Trial: The resulting trajectory when an agent stop at some terminal state.
   • Episodic v.s. Continuing Tasks.
   • Episodic tasks can be converted to Continuing Tasks.

Example: Markov Decision Process (MDP)

1. Sets
   • State: \(\mathcal{S}\)
   • Action: \(\mathcal{A}(s_i)\)
   • Reward: \(\mathcal{R(s, a)}\)
2. Probability Distributions
   • State Transition Probability: \(p(s' \vert s, a)\)
   • Reward Probability: \(p(r \vert s, a)\)
3. Policy: At state \(s\), the probability to choose the action \(a\) is \(\pi (a \vert s)\)
4. Markov Property: memoryless
   • \(p(s_{t+1} \vert a_{t+1}, s_t, \dots, a_1, s_0) = p(s_{t+1} \vert a_{t+1}, s_t)\)
   • \(p(r_{t+1} \vert a_{t+1}, s_t, \dots, a_1, s_0) = p(r_{t+1} \vert a_{t+1}, s_t)\)

Bellman Equation

Return

Bootstraping of Returns

\[\displaylines{ \textcolor{red}{v_1} = r_1 + \gamma v_2\\ v_2 = r_1 + \gamma v_3\\ v_3 = r_1 + \gamma v_4\\ v_4 = r_1 + \gamma \textcolor{red}{v_1} }\]

Matrix-vector Form

\[\underbrace{\begin{bmatrix} v_1 \\ v_2 \\ v_3 \\ v_4 \\ \end{bmatrix}}_{v} = \underbrace{\begin{bmatrix} r_1 \\ r_2 \\ r_3 \\ r_4 \\ \end{bmatrix}}_{r} + \gamma \underbrace{\begin{bmatrix} 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \\ 1 & 0 & 0 & 0 \\ \end{bmatrix}}_{P} \underbrace{\begin{bmatrix} v_1 \\ v_2 \\ v_3 \\ v_4 \\ \end{bmatrix}}_{v}\] \[\begin{align} v &= r + \gamma P v \nonumber\\ (I - \gamma P) v &= r \nonumber \end{align}\]

State Value

Consider the following multi-step trajectory:

\[S_t \xrightarrow[]{A_t} R_{t+1}, S_{t+1} \xrightarrow[]{A_{t+1}} R_{t+2}, S_{t+2} \xrightarrow[]{A_{t+2}} R_{t+3}, S_{t+3} \xrightarrow[]{} \dots\]

where:

• \(S_t \rightarrow A_t\) is governed by \(\pi (A_t = a \vert S_t = s)\)
• \(S_t, A_t \rightarrow R_{t+1}\) is governed by \(p(R_{t+1} = r \vert S_t = s, A_t = a)\)
• \(S_t, A_t \rightarrow S_{t+1}\) is governed by \(p(S_{t+1} = s' \vert S_t = s, A_t = a)\)

whose discounted return is:

\[\begin{align} G_t &= R_{t+1} + \gamma R_{t+2} + \gamma ^2 R_{t+3} + \dots \nonumber\\ &= R_{t+1} + \gamma (R_{t+2} + \gamma R_{t+3} + \dots) \nonumber\\ & = R_{t+1} + \gamma G_{t+1} \nonumber \end{align}\]

State value (or state-value function) is defined as the expectation of the discounted return

\[\begin{align} v(\pi, s) \rightarrow v_{\pi}(s) &= \mathbb{E}[G_t \vert S_t = s] \nonumber\\ &= \mathbb{E}[R_{t+1} + \gamma G_{t+1} \vert S_t = s] \nonumber\\ &= \underbrace{\mathbb{E}[R_{t+1} \vert S_t = s]}_{\text{Term I}} + \underbrace{\gamma \mathbb{E} [G_{t+1} \vert S_t = s]}_{\text{Term II}} \nonumber \end{align}\]

• State value describe how valuable a state is.
• The mean of all possible returns.

Bellman Equation

Term I

\[\begin{align} \mathbb{E}[G_t \vert S_t = s] &= \sum_{a} \pi (a \vert s) \mathbb{E} [R_{t+1} \vert S_t = s, A_t = a] \nonumber\\ &= \sum_{a} \pi (a \vert s) \sum_{r} p(r|s, a) r \nonumber \end{align}\]

• At state \(s\) there are actions \({a_i}\) and the probability to take an action \(a\) is \(\pi (a \vert s)\)
• After taking action \(a\), the return is \(\mathbb{E} [R_{t+1} \vert S_t = s, A_t = a]\)
• The mean of immediate reward.

Term II

\[\begin{align} \mathbb{E}[G_{t+1} \vert S_t = s] &= \sum_{s'} p(s' \vert s) \mathbb{E}[G_{t+1} \vert \textcolor{red}{S_t = s}, S_{t+1} = s'] \nonumber\\ &= \sum_{s'} p(s' \vert s) \mathbb{E}[G_{t+1} \vert S_{t+1} = s'] \nonumber\\ &= \sum_{s'} p(s' \vert s) v_{\pi}(s') \nonumber\\ &= \sum_{s'} v_{\pi}(s') \sum_{a} p(s' \vert s, a) \pi(a \vert s) \nonumber \end{align}\]

• \(\mathbb{E}[G_{t+1} \vert \textcolor{red}{S_t = s}, S_{t+1} = s'] = \mathbb{E}[G_{t+1} \vert S_{t+1} = s']\) since the return is not dependent on previous steps.
• \(p(s' \vert s) = \sum_{a} p(s' \vert s, a) \pi(a \vert s) \longleftarrow\) Law of total probability
• The mean of future reward.

Final form

\[\begin{align} \textcolor{red}{v_{\pi}(s)} &= \mathbb{E}[R_{t+1} \vert S_t = s] + \gamma \mathbb{E} [G_{t+1} \vert S_t = s] \nonumber\\ &= \sum_{a} \pi (a \vert s) \sum_{r} p(r|s, a) r + \gamma \sum_{s'} v_{\pi}(s') \sum_{a} p(s' \vert s, a) \pi(a \vert s) \nonumber\\ &= \sum_{a} \pi (a \vert s) [\sum_{r} p(r|s, a) r + \gamma \sum_{s'} \textcolor{red}{v_{\pi}(s')} p(s' \vert s, a) ], \textcolor{green}{\forall s \in S}. \nonumber\\ &= \sum_{a} \underbrace{\pi (a \vert s)}_{\text{A given policy}} [\sum_{r} \underbrace{p(r|s, a) r}_{\text{Dynamic model}} + \gamma \sum_{s'} \underbrace{p(s' \vert s, a)}_{\text{Dynamic model}} \textcolor{red}{v_{\pi}(s')} ], \textcolor{green}{\forall s \in S}. \nonumber \end{align}\]

• Solving the Bellman equation is called Policy Evaluation.

Matrix-vector Form

\[\begin{align} \textcolor{red}{v_{\pi}(s)} &= \sum_{a} \pi (a \vert s) (\sum_{r} p(r|s, a) r + \gamma \sum_{s'} p(s' \vert s) v_{\pi}(s')) \nonumber\\ &= r_{\pi}(s) + \gamma \sum_{s'} p(s' \vert s) v_{\pi}(s'), \textcolor{green}{\forall s \in S} \nonumber\\ \textcolor{red}{v_{\pi}(s_i)} &= r_{\pi}(s_i) + \gamma \sum_{s_j} p(s_j \vert s_i) v_{\pi}(s_j), \textcolor{green}{\forall s_i \in S} \nonumber\\ &= r_{\pi} + \gamma P v_{\pi} \nonumber \end{align}\]

where

• \(P \in \mathbb{R}^{n \times n}, P_{ij} = p(s_j \vert s_i)\) is the State Transition Matrix.

Solve Bellman Equation

Closed-form solution

\[v_{\pi} = (I - \gamma P)^{-1} r_{\pi}\]

Iterative solution

\[v_{k+1} = r_{\pi} + v_{k}\]

• It can be proved: \(v_k \rightarrow v_{\pi} = (I - \gamma P)^{-1} r_{\pi}, k \rightarrow \infty\).

Action Value

\[\begin{align} q(\pi, s, a) \rightarrow q_{\pi}(s, a) &= \mathbb{E}[G_t \vert S_t = s, A_t = a] \nonumber\\ &= \underbrace{\mathbb{E}[G_{t} \vert S_t = s]}_{v_{\pi}(s)} \nonumber\\ &= \sum_{a} \pi(a \vert s) \underbrace{\mathbb{E}[G_t \vert S_t=s, A_t = a]}_{q_{\pi}(s, a)} \nonumber\\ v_{\pi}(s) &= \sum_{a} \pi (a \vert s) \underbrace{[\sum_{r} p(r|s, a) r + \gamma \sum_{s'} p(s' \vert s, a) v_{\pi}(s') ]}_{q_{\pi}(s, a)} \nonumber\\ \end{align}\]

Therefore:

\[q_{\pi}(s, a) = \sum_{r} p(r|s, a) r + \gamma \sum_{s'} p(s' \vert s, a) v_{\pi}(s') \nonumber\]

Bellman Optimality Equation

Optimal Policy

\[\text{A policy } \pi^{*} \text{ is optimal if } v_{\pi^{*}}(s) \geq v_{\pi}(s), \forall s \in S\]

• Does the optimal polcy exist?
• Is the optimal polcy unique?
• Is the optimal polcy stochastic or deterministic?
• How to obtain optimal polcy?

Bellman Optimality Equation

\[\begin{align} v_{\pi}(s) &= \textcolor{red}{\max_{\pi}} \sum_{a} \textcolor{red}{\pi (a \vert s)} (\sum_{r} p(r|s, a) r + \gamma \sum_{s'} p(s' \vert s, a) v_{\pi}(s')) \nonumber\\ & = \textcolor{red}{\max_{\pi}} \sum_{a} \textcolor{red}{\pi (a \vert s)} q(s, a) \nonumber \end{align}\]

Considering \(\displaystyle \sum_{a} \pi (a \vert s) = 1\), we have

\[\max_{\pi} \sum_{a} \pi (a \vert s) q(s, a) = \max_{a \in \mathcal{A(s)}} q(s, a)\]

where the optiamality is achieved when

\[\pi (a \vert s) = \begin{cases} 1, a = a^{*} \\ 0, a \neq a^{*} \\ \end{cases}\]

where \(\displaystyle a^{*} = \arg \underset{a}{\max} \ q(s, a)\)

Matrix-vector Form

\[v= \max_{\pi}(r_{\pi} + \gamma P_{\pi}v)\]

Banach Fixed-point Theorem

\[\begin{align} v_{\pi}(s) &= \textcolor{red}{\max_{\pi}} \sum_{a} \textcolor{red}{\pi (a \vert s)} (\sum_{r} p(r|s, a) r + \gamma \sum_{s'} p(s' \vert s, a) v_{\pi}(s')) \nonumber\\ &= \textcolor{red}{\max_{\pi}} (r_{\pi} + \gamma P_{\pi} v) \nonumber \end{align}\] \[\Longrightarrow v = f(v), \text{where } [f(v)]_s = \max_{\pi} \sum_{a} \pi (a \vert s) q(s, a) \nonumber\]

Also known as Contraction/Contractive Mapping Theorem or Banach–Caccioppoli theorem. (Wikipedia)

Contraction Mapping

Let \((X, d)\) be a metric space. Then a map \(T: X \rightarrow X\) is called a contraction mapping on \(X\) if \(\exists q \in [0, 1)\) such that

\[d(T(x), T(y)) \leq q d(x, y),\forall x, y \in X.\]

Banach Fixed-point theorem

Let \((X, d)\) be a non-empty complete metric space with a contraction mapping \(T: X \rightarrow X\). Then \(T\) admits a unique fixed-point \(x^{*} \in X\) (i.e. \(T(x^{*}) = x^{*}\) ).

Further more, \(x^{*}\) can be found as follows: start with an arbitary element \(x_0 \in X\) and define a sequence \((x_n)_{n \in \mathbb{N}}\) for \(n \geq 1\). Then \(\displaystyle \lim_{n \rightarrow \infty} x_n = x^{*}\).

• Exponential convergence speed. (Lipschitz constant)
• \(q\) must be strictly less than 1.

Greedy Optimal Policy

For any \(s \in S\), the deterministic greedy policy

\[\pi^{*} (a \vert s) = \begin{cases} 1, a = a^{*} \\ 0, a \neq a^{*} \\ \end{cases}\]

is an optimal policy solving the BOE.

Here \(\displaystyle a^{*} = \arg \underset{a}{\max} \ q^{*}(s, a)\) where \(q^{*}(s, a) := \sum_r p(r \vert s, a) r + \gamma \sum_{s'} p(s' \vert s, a) v^{*}(s')\)

Some Analysis

\[\begin{align} \textcolor{red}{v(s)} &= \max_{\pi} \sum_{a} \textcolor{red}{\pi (a \vert s)} (\sum_{r} \textcolor{green}{p(r|s, a) r} + \textcolor{green}{\gamma} \sum_{s'} \textcolor{green}{p(s' \vert s, a)} \textcolor{red}{v(s')}) \nonumber \end{align}\]

Known factors

• Reward design: \(r\)
• System model: \(p(r \vert s, a) r, p(s' \vert s, a)\)
• Discount rate: \(\gamma\)

Unknow factors

• \(v(s), v(s')\) and \(\pi(a \vert s)\)

Value & Policy Iteration

Model-based

Matrix-vector form is useful for theoretical analysis and Elementwise form is useful for implementation.

Value Iteration

\[\begin{align} v_{k+1} &= f(v_{k}) \nonumber\\ &= \max_{\pi} (r_{\pi} + \gamma P_{\pi} v_{k}), k = 1, 2, 3 \dots \nonumber\\ &= \max_{\pi} \sum_{a} \pi (a \vert s) (\sum_{r} p(r|s, a) r + \gamma \sum_{s'} p(s' \vert s, a) v_{\pi}(s')) \nonumber \end{align}\]

Step 1: Policy Update

\[\begin{align} \pi_{k+1} &= \arg \underset{\pi}{\max} (r_{\pi} + \gamma P_{\pi} v_{k}), (v_{k} \text{ is given.}) \nonumber\\ \pi_{k+1}(s) &= \arg \underset{\pi}{\max} \sum_{a} \pi(a \vert s) (\sum_{r}p(r \vert s, a) r + \gamma \sum_{s'} p(s' \vert s, a) v_k(s')), s \in S \nonumber \end{align}\]

Step 2: Value Update

\[\begin{align} v_{k+1} &= r_{\pi_{k+1}} + \gamma P_{\pi_{k+1}} v_{k} \nonumber\\ v_{k+1}(s) &= \sum_{a} \pi_{k+1} (a \vert s) \underbrace{(\sum_{r} p(r \vert s, a)r + \gamma \sum_{s'} p(s' \vert s, a) v_k(s'))}_{q_k (s,a)}, s \in S \nonumber\\ &= \max_{a} q_k(s, a) \nonumber \end{align}\]

• \(v_k\) is not a state value. It’s merely an intermediate value generated by the algorithm. Therefore, \(q_k\) is not an action value either.

Policy Iteration

Step 1: Policy Evaluation

\[\begin{align} v_{\pi_k} &= r_{\pi_k} + \gamma P_{\pi_k} v_{\pi_k} \nonumber\\ v_{\pi_k}^{j+1}(s) &= \sum_{a} \pi_k (a \vert s) (\sum_{r} p (r \vert s, a) r + \gamma \sum_{s'} p(s' \vert s, a) v_{\pi_k}^{j} (s')), s \in S, j = 1, 2, \dots \nonumber \end{align}\]

Step 2: Policy Improvement

\[\begin{align} \pi_{k+1} &= \arg \max_{\pi} (r_{pi} + \gamma P_{\pi} v_{\pi_k}) \nonumber\\ \pi_{k+1}(s) &= \arg \max_{\pi} \sum_{a} \pi(a \vert s) (\sum_{r} p(r \vert s, a)r + \gamma \sum_{s'} p(s' \vert s, a) v_k(s')), s \in S \nonumber \end{align}\]

Truncated policy iteration

Value Iteration and Policy Iteration algorithms are two special cases of the truncated policy iteration algorithm.

\[\begin{align} v_{\pi 1}^{(0)} &= v_0 \nonumber\\ v_{\pi 1}^{(1)} &= r_{\pi 1} + \gamma P_{\pi 1} v_{\pi 1}^{(0)} \longrightarrow v_1 \longrightarrow \text{Value Iteration} \nonumber\\ v_{\pi 1}^{(2)} &= r_{\pi 1} + \gamma P_{\pi 1} v_{\pi 1}^{(1)} \nonumber\\ \vdots \nonumber\\ v_{\pi 1}^{(j)} &= r_{\pi 1} + \gamma P_{\pi 1} v_{\pi 1}^{(j-1)} \longrightarrow \bar{v}_1 \longrightarrow \text{Truncated Policy Iteration} \nonumber\\ \vdots \nonumber\\ v_{\pi 1}^{(\infty)} &= r_{\pi 1} + \gamma P_{\pi 1} v_{\pi 1}^{(\infty)} \longrightarrow v_{\pi 1} \longrightarrow \text{Policy Iteration} \nonumber \end{align}\]

Monte Carlo Methods

Model-free

Convert the Policy Iteration algorithm to be model-free.

Monte Carlo Basic

\[\begin{align} q(\pi, s, a) \rightarrow q_{\pi}(s, a) &= \mathbb{E}[G_t \vert S_t = s, A_t = a] \longrightarrow \text{Model-free} \nonumber\\ &= \sum_{r} p(r|s, a) r + \gamma \sum_{s'} p(s' \vert s, a) v_{\pi}(s') \longrightarrow \text{Model-based} \nonumber \end{align}\]

Step 1: Policy Evaluation

• Estimate \(q_{\pi_k}(s, a)\) directly, instead of sovling \(v_{\pi_k}(s)\)

Step 2: Policy Improvement

• Same as Policy Iteration

Monte Carlo Exploring Starts

Visit: Every time a state-action pair appears in one episode is a visit of that state-action pair.

Data-efficient Methods

1. First-visit method: Use the return of the first visit of a state-value pair to estimate \(q_{\pi_k}(s, a)\)

2. Every-visit method: \(\dots\) every visit of a state-value pair \(\dots\)

3. Dynamic Programming in implementation.

Generalized Policy Iteration

Switch between the policy-evaluation and policy-improvement processes.

Monte Carlo Exploring Starts

• Exploring (all state-action pairs)
  1. as Start
  2. via Visit (can’t be guaranteed yet)

Monte Carlo \(\epsilon\)-Greedy

Soft Policy: The probability to take any action is positive. Hence, the requirement of Exploring Starts can be removed.

\(\epsilon\)-Greedy Policy

\[\pi (a \vert s) = \begin{cases} \displaystyle 1 - \frac{\epsilon}{ \vert \mathcal{A}(s) \vert} (\vert \mathcal{A}(s) \vert - 1) \nonumber\\ \displaystyle \frac{\epsilon}{ \vert \mathcal{A}(s) \vert} \nonumber \end{cases}\]

where \(\epsilon \in [0, 1]\) and \(\vert \mathcal{A}(s) \vert\) is the number of actions for \(s\).

Exploitation versus Exploration

\[0 \xleftarrow[]{Exploitation!} \epsilon \xrightarrow[]{Exploration!} 1\]

Monte Carlo \(\epsilon\)-Greedy

• Use every-visit method.

Stochastic Approxmimation 🐾

Stochastic Approxmimation refers to a broad class of stochastic and iterative algorithms solving root-finding or optimization problems. (From non-incremental to incremental methods.)

• No expression of the objective function required.
• No derivative required.

Mean Estimation

\[\begin{align} w_{k+1} &= \frac{1}{k} \sum_{i=1}^{k} x_i, i = 1, 2, \dots \nonumber\\ &= w_k - \frac{1}{k} (w_k - x_k) \nonumber \end{align}\]

• Obtain an estimation when a sample is received.
• The estimate is not accurate at the beginning due to small number of samples. But it’s better than nothing and it can imporve gradually.

Robbins-Monro Algorithm

Suppose \(J(w)\) is a unknown objective function to be optimized. And the problem we want to solve is:

\[g(w) := \nabla_w J(w) = 0\]

The Robbins-Monro Algorithm

\[w_{k+1} = w_{k} - a_k \tilde{g} (w_k, \eta_k)\]

can solve the problem where

• \(w_k\) is the \(k\)-th estimation of the root.
• \(a_k\) is a possitive coefficient.
• \(\tilde{g} (w_k, \eta_k) = g(w_k) + \eta_k\) is the \(k\)-th noisy observation.

Robbins-Monro Theorem

\(w_k\) converges with probability 1 (w.p.1) (almost sure convergence) to the root \(w^{*}\) satisfying \(g(w^{*}) = 0\) if

1. \(0 < c_1 \leq \nabla_w g(w) \leq c_2\) for all \(w\)
   • \(g(w)\) is monotonically increasing \(\Longrightarrow g(w) = 0\) exists and is unique (\(\Longrightarrow\) Convexity of objective function \(H(x) = \nabla_w^2 J(w)\))
   • \(\nabla_w g(w)\) has an upperbound
2. \(\displaystyle \sum_{k=1}^{\infty} a_k = \infty\) and \(\displaystyle \sum_{k=1}^{\infty} a_k^2 < \infty\)
   • \(\displaystyle \sum_{k=1}^{\infty} a_k = \infty\) ensures \(a_k\) do not converge to zero too fast
   • \(\displaystyle \sum_{k=1}^{\infty} a_k^2 < \infty\) ensures \(a_k\) converges to zero as \(k \rightarrow \infty\)
3. \(\mathbb{E} [\eta_k \vert \mathcal{H_k}] = 0\) and \(\mathbb{E} [\eta_k^2 \vert \mathcal{H_k}] = 0\), where \(\mathcal{H_k} = \{w_k, w_{k-1}, \dots\}\)
   • \(\eta_k\) has a mean of zero and bounded variance, such as Independent and identically distributed (iid)

Example

In the mean estimation problem, \(a_k = \frac{1}{k}\)

• \(\displaystyle \lim_{n \to \infty } (\sum_{k=1}^{n} \frac{1}{k} - \ln n) = \kappa \approx 0.577\), the Euler-Mascheroni constant
• \(\displaystyle \sum_{k=1}^{\infty} \frac{1}{k^2} = \frac{\pi^2}{6} < \infty\), the Basel Problem in number theory

Dvoretzky’s Theorem (Wikipedia)

Stochastic Gradient Descent

For the following optimization problem:

\[\min_{w} J(w) = \mathbb{E} [f(w, X)]\]

• \(w\) is the parameter to be optimized
• \(X\) is a random variable
• \(w\) and \(X\) can be scalars or vectors, \(f(\cdot)\) is a scalar

Gradient Descent

\[\begin{align} w_{k+1} &= w_k - \alpha_k \nabla_w \mathbb{E} [f(w_k, X)] \nonumber\\ &= w_k - \alpha_k \mathbb{E} [\nabla_w f(w_k, \textcolor{red}{X})]\nonumber \end{align}\]

Batch Gradient Descent

\[\begin{align} \mathbb{E} [\nabla_w f(w_k, \textcolor{red}{X})] &\approx \frac{1}{n} \sum_{i=1}^{n} [\nabla_w f(w_k, \textcolor{red}{x_i})] \nonumber\\ w_{k+1} &\approx w_k - \alpha_k \frac{1}{n} \sum_{i=1}^{n} [\nabla_w f(w_k, \textcolor{red}{x_i})] \nonumber \end{align}\]

Mini Batch Gradient Descent

\[\begin{align} \mathbb{E} [\nabla_w f(w_k, \textcolor{red}{X})] &\approx \frac{1}{m} \sum_{x_j \in X} [\nabla_w f(w_k, \textcolor{red}{x_j})] \nonumber\\ w_{k+1} &\approx w_k - \alpha_k \frac{1}{m} \sum_{x_j \in X} [\nabla_w f(w_k, \textcolor{red}{x_j})] \nonumber \end{align}\]

Stochastic Gradient Descent

\[w_{k+1} = w_k - \alpha_k \mathbb{E} [\nabla_w f(w_k, \textcolor{red}{x_k})]\]

• Replace ture gradient \(\nabla_w f(w_k, \textcolor{red}{X})\) with the stochastic gradient \(\nabla_w f(w_k, \textcolor{red}{x_k})\)
• BGD \((n=1) \Longrightarrow\) SGD

A special case of the Robbins-Monro Algorithm.

\[\begin{align} \tilde{g} (w, \eta) &= \nabla_w f(w, x) \nonumber\\ & = \underbrace{\mathbb{E} [\nabla_w f(w_k, \textcolor{red}{X})]}_{g(w)} + \underbrace{\nabla_w f(w, x) - \mathbb{E} [\nabla_w f(w_k, \textcolor{red}{X})]}_{\eta} \nonumber \end{align}\]

Convergence Pattern

Relative error

\[\begin{align} \delta_k &= \frac{\vert \nabla_w f(w_k, x_k) - \mathbb{E} [\nabla_w f(w_k, X)] \vert}{\vert \mathbb{E} [\nabla_w f(w_k, X)] \vert} \nonumber\\ &= \frac{\vert \nabla_w f(w_k, x_k) - \mathbb{E} [\nabla_w f(w_k, X)] \vert}{\vert \mathbb{E} [\nabla_w f(w_k, X)] - \{\mathbb{E}[\nabla_w f(w^{*}, X)] = 0\} \vert} \nonumber\\ &= \frac{\vert \nabla_w f(w_k, x_k) - \mathbb{E} [\nabla_w f(w_k, X)] \vert}{\vert \mathbb{E} [\nabla_w^2 f(w_k, X)(w_k - w^{*})] \vert} \Longleftarrow \nonumber\\ &\leq \frac{\vert \overbrace{\nabla_w f(w_k, x_k)}^{\text{SG}} - \overbrace{\mathbb{E} [\nabla_w f(w_k, X)]}^{TG} \vert}{\underbrace{c \vert w_k - w^{*} \vert}_{\text{Distance to } w^{*}}} \nonumber\\ \end{align}\]

• \(\Longleftarrow\) Apply mean value theorem (\(f(x_1) - f(x_2) = f'(x_3)(x_1 - x_2)\))
• \(\delta_k\) is inversely proportional to \(\vert w_k - w^{*} \vert\)
• When \(w_k\) is far away from \(w^{*}\), \(\delta_k\) is small and SGD behaves like GD
• When \(w_k\) is close to \(w^{*}\), \(\delta_k\) is large and SGD exhibits more randomness

Temporal Difference Learning

Temporal Difference Learning often refers to a broad class of RL algorithms and/or a specific algorithm for estimationg state value.

TD Learning

TD Learning for State Value.

The goal is to estimate \(v_{\pi}(s), \forall s \in \mathcal{S}\) given policy \(\pi\). The TD algorithm for \({(s_t, r_{t+1}, s_{t+1})}_t\) is

\[\underbrace{v_{t+1}(s_t)}_{\text{New Estimation}} = \underbrace{v_t(s_t)}_{\text{Current Estimation}} - \alpha_t(s_t) \overbrace{[v_t(s_t) - \underbrace{(r_{t+1} + \gamma v_t(s_{t+1}))}_{\text{TD Target }\bar{v}_t}]}^{\text{TD Error } \delta_t}\]

• \(v_t(s_t)\) is the estimated State Value of \(v_{\pi}(s_t)\) at time step \(t\)
• \(\alpha_t(s_t)\) is the learning rate at time step \(t\)
• \(v_{t+1}(s) = v_t(s), \forall s \neq s_t\) is usually omitted for unvisited states
• A special sotchatic approximation algorithm for solving the Bellman equation
• Does NOT estimate action value
• Does NOT search for the optimal policy

TD Target

\(v(s_t)\) is driven towards \(\bar{v}_t:= r_{t+1} + \gamma v_t(s_{t+1})\)

\[\begin{align} v_{t+1}(s_t) &= v_t(s_t) - \alpha_t(s_t)[v_t(s_t) - \bar{v}_t] \nonumber\\ v_{t+1}(s_t) - \bar{v}_t &= (v_t(s_t) - \bar{v}_t) - \alpha_t(s_t)[v_t(s_t) - \bar{v}_t]\nonumber\\ \vert v_{t+1}(s_t) - \bar{v}_t \vert &= \underbrace{\vert 1 - \alpha_t(s_t) \vert}_{\in (0, 1)} \vert v_t(s_t) - \bar{v}_t \vert \nonumber\\ \vert v_{t+1}(s_t) - \bar{v}_t \vert &< \vert v_t(s_t) - \bar{v}_t \vert \nonumber \end{align}\]

TD Error

• The discrepancy between two consequent time steps \(\Longrightarrow\) TD
• Reflects the deficiency between \(v_t\) and \(v_{\pi}\)
• New information (Innovation) obtained from the experience \({(s_t, r_{t+1}, s_{t+1})}_t\)

Convergence

\(v_t(s)\) converges w.p.1 to \(v_{\pi}(s), \forall s \in \mathcal{S}\) as \(t \to \infty\), if \(\displaystyle \sum_t \alpha_t (s) = \infty\) and \(\displaystyle \sum_t \alpha_t^2 (s) < \infty, \forall s \in \mathcal{S}\)

• State value can be found for a given policy \(\pi\)
• Every state must be visited an infinite(sufficiently large) number of times
• \(\alpha\) is typically set as a small constant (not zero) because later experience is expected to influence the state value (TD algorithm can still converge in the sense of expectation)

Sarsa

TD Learning for Action Value. State-Action-Reward-State-Action \((s_t, a_t, r_{t+1}, s_{t+1}, a_{t+1})\)

\[\begin{align} q_{t+1}(s_t, a_t) &= q_t(s_t, a_t) - \alpha_t(s_t, a_t) [q_t(s_t, a_t) - [r_{t+1} + \gamma q_t(s_{t+1}, a_{t+1})]] \nonumber\\ q_{t+1}(s, a) &= q_t(s, a), \forall (s, a) \neq (s_t, a_t) \nonumber \end{align}\]

Mathematically, the expression suggests it’s a stochastic approximation algorithm solving the following Bellman equation

\[q_{\pi}(s, a) = \mathbb{E} [R + \gamma q_{\pi}(S', A') \vert (s, a)], \forall (s, a)\]

Convergence

\(q_t(s, a)\) converges w.p.1 to \(q_{\pi}(s, a), \forall (s, a)\) as \(t \to \infty\), if \(\displaystyle \sum_t \alpha_t (s, a) = \infty\) and \(\displaystyle \sum_t \alpha_t^2 (s, a) < \infty, \forall (s, a) \in \mathcal{S}\)

• Policy Evaluation
• Action value can be found for a given policy \(\pi\)

Add Policy Improvement

\[\pi_{t+1}(a \vert s_t) = \begin{cases} 1 - \frac{\epsilon}{\vert \mathcal{A} \vert} (\vert \mathcal{A} \vert - 1), a = \arg \max_a q_{t+1}(s_t, a) \\ \frac{\epsilon}{\vert \mathcal{A} \vert}, \text{otherwise} \end{cases}\]

• Often also called Sarsa after adding policy improvement
• Not all states have the optimal policy

Expected Sarsa

\[\begin{align} q_{t+1}(s_t, a_t) &= q_t(s_t, a_t) - \alpha_t(s_t, a_t) [q_t(s_t, a_t) - r_{t+1} + \gamma \mathbb{E} [q_t(s_{t+1}, A)]] \nonumber\\ q_{t+1}(s, a) &= q_t(s, a), \forall (s, a) \neq (s_t, a_t) \nonumber \end{align}\]

• \(\displaystyle \mathbb{E} [q_t(s_{t+1}, A)] = \sum_a \pi_t(a \vert s_{t+1}) q_t(s_{t+1}, a) := v_t(s_{t+1})\), which is the state value of \(s_{t+1}\)
• Reduced estimation variance becuase the number of r.v. is reduced (\((s_t, a_t, r_{t+1}, s_{t+1}, a_{t+1}) \Longrightarrow (s_t, a_t, r_{t+1}, s_{t+1})\))

Mathematically

\[q_{\pi}(s, a) = \mathbb{E} [R_{t+1} + \gamma v_{\pi}(S_{t+1}) \vert (S_t = s, A_t = a)]\]

\(n\)-step Sarsa

Can unify Sarsa and Monte Carlo learning.

The discounted return \(G_t\) can be written as

\[\begin{align} G_t &= \gamma^{0}R_{t+1} + \gamma^{1}q_{\pi}(S_{t+1}, A_{t+1}) \longrightarrow \text{Sarsa} \nonumber\\ G_t &= \gamma^{0}R_{t+1} + \gamma^{1}R_{t+2} + \gamma^{2} q_{\pi}(S_{t+2}, A_{t+2})\nonumber\\ &\vdots \nonumber\\ G_t &= \gamma^{0}R_{t+1} + \gamma^{1}R_{t+2} + \cdots + \gamma^{n}q_{\pi}(S_{t+n}, A_{t+n}) \longrightarrow n\text{-Step Sarsa}\\ &\vdots \nonumber\\ G_t &= \gamma^{0}R_{t+1} + \gamma^{1}R_{t+2} + \gamma^{2}R_{t+3} + \cdots \longrightarrow \text{Monte Carlo} \nonumber \end{align}\]

Q-Learning

TD Learning for Optimal Action Value. State-Action-Reward-State-Action \((s_t, a_t, r_{t+1}, s_{t+1}, a_{t+1})\)

\[\begin{align} q_{t+1}(s_t, a_t) &= q_t(s_t, a_t) - \alpha_t(s_t, a_t) [q_t(s_t, a_t) - [r_{t+1} + \gamma \max_{a \in \mathcal{A}}(s_{t+1}, a)]] \nonumber\\ q_{t+1}(s, a) &= q_t(s, a), \forall (s, a) \neq (s_t, a_t) \nonumber \end{align}\]

Mathematically

\[q(s, a) = \mathbb{E} [R_{t+1} + \gamma \max_{a} q(S_{t+1}, a) \vert (S_t = s, A_t = a)], \forall s, a\]

• The Bellman optimality equation expressed in terms of action value

On/Off-Policy

Behavior Policy is the policy that the agent follows while interacting with the environment and collecting data. Target Policy is the policy that the agent is trying to learn and improve.

On-policy: Behavior Policy = Target Policy. For example, in Sarsa, the agent updates q value based on actions it actually took (according to its current policy).

Off-policy: Behavior Policy ≠ Target Policy. For example, in Q-Learning, the behavior policy CAN be different from the target policy. The target policy is the one learning to optimize, while the behavior policy is the one used for exploration and interaction.

Value Function Approximation

Value Representation

\[(s_i, a_i)_{i=1}^{n} \Longrightarrow \hat{v}(s, w) = \phi^T(s) w\]

• \(\phi^T(s)\) is the Feature Vector
• \(w\) is the Parameter Vector
• More storage efficiency by sacrificing accuracy
• More generalization ability

Objective Function

The objective is to find optimal \(w\) such that \(\hat{v}(s, w) \approx v_{\pi}(s)\), which makes it a Policy Evaluation problem.

\[J(w) = \mathbb{E}[(v_{\pi}(S) - \hat{v}(S, w))^2]\]

• Find the best \(w\) that minimize \(J(w)\)
• \(\mathbb{E}\) is with respect to the r.v. \(S \in \mathcal{S}\). So the distribution of \(S\) matters.

Uniform Distribution of \(S \in \mathcal{S}\)

\[\begin{align} J(w) &= \mathbb{E}[(v_{\pi}(S) - \hat{v}(S, w))^2] \nonumber\\ &= \frac{1}{\vert \mathcal{S} \vert} \sum_{s \in \mathcal{S}}(v_{\pi}(s) - \hat{v}(s, w)^2) \nonumber \end{align}\]

• Not representing the real dynamics of the Markov process under the given policy (not all states are equally important)

Stationary Distribution of \(S \in \mathcal{S}\)

Stationary Distribution (Wikipedia)

\[\begin{align} J(w) &= \mathbb{E}[(v_{\pi}(S) - \hat{v}(S, w))^2] \nonumber\\ &= \sum_{s \in \mathcal{S}}d_{\pi}(s)(v_{\pi}(s) - \hat{v}(s, w)^2) \nonumber \end{align}\]

Optimize \(J(w)\)

\[w_{k+1} = w_{k} - \alpha_k\nabla_wJ(w_k)\]

True Gradient

\[\begin{align} \nabla_w J(w_k) &= \nabla_w \mathbb{E}[(v_{\pi}(S) - \hat{v}(S, w))^2] \nonumber\\ &= 2\mathbb{E}[(v_{\pi}(S) - \hat{v}(S, w))(-\nabla_w\hat{v}(S, w))] \nonumber\\ &= -2\textcolor{red}{\mathbb{E}}[(v_{\pi}(S) - \hat{v}(S, w))\nabla_w\hat{v}(S, w)] \nonumber \end{align}\]

Stochastic Gradient

\[\begin{align} w_{k+1} &= w_{k} - \alpha_k (-2\textcolor{red}{\mathbb{E}}[(v_{\pi}(S) - \hat{v}(S, w))\nabla_w\hat{v}(S, w)]) \nonumber\\ &= w_{k} + \underbrace{\textcolor{green}{\alpha_k}}_{2\alpha_k}[\textcolor{red}{v_{\pi}(s_t)} - \hat{v}(s_t, w_t)]\nabla_w\hat{v}(S, w) \nonumber \end{align}\]

Approximate \(v_{\pi}(s_t)\)

1. Monte Carlo: \(w_{k+1} = w_{k} + \textcolor{green}{\alpha_k}[\textcolor{red}{g_t} - \hat{v}(s_t, w_t)]\nabla_w\hat{v}(S, w)\)

2. TD Learning: \(w_{k+1} = w_{k} + \textcolor{green}{\alpha_k}[\textcolor{red}{r_{t+1} + \gamma \hat{v}(s_{t+1}, w_t) } - \hat{v}(s_t, w_t)]\nabla_w\hat{v}(S, w)\)

Sarsa

\[w_{k+1} = w_{k} + \alpha_k[r_{t+1 + \gamma \hat{q}(s_{t+1}, w_t) } - \hat{q}(s_t, w_t)]\nabla_w\hat{q}(S, w)\]

Q-Learning

\[w_{k+1} = w_{k} + \alpha_k[r_{t+1} + \gamma \max_{a \in \mathcal{A}(s_{s+1})}\hat{q}(s_{t+1},a, w_t) - \hat{q}(s_t, a_t, w_t)]\nabla_w\hat{q}(s_t, a_t, w_t)\]

Deep Q-Learning

Deep Q-Learning or Deep Q-Network (DQN).

• Nueral Network is a nonlinear function approximator
• Different from Q-Learning because it requires low level calculation of \(\nabla_w\hat{q}(s_t, a_t, w_t)\)

Objective Function

Mathematically, Q-Learning is solving the following Bellman Optimality Equation

\[q(s, a) = \mathbb{E} [R_{t+1} + \gamma \max_{a} q(S_{t+1}, a) \vert (S_t = s, A_t = a)], \forall s, a\]

So, the objective function is

\[\begin{align} J(w) &= \mathbb{E}[(R + \gamma \max_{a \in \mathcal{A}(S')}\textcolor{red}{\hat{q}(S', a, w)} - \textcolor{blue}{\hat{q}(S, A, w)})^2] \nonumber \\ J(w) &= \mathbb{E}[(R + \gamma \max_{a \in \mathcal{A}(S')}\textcolor{red}{\hat{q}(S', a, w_T)} - \textcolor{blue}{\hat{q}(S, A, w)})^2] \nonumber \end{align}\]

Optimize \(J(w)\)

\[\nabla_w J(w) = \mathbb{E} [(R + \gamma \max_{a \in \mathcal{A}(S')} \textcolor{red}{\hat{q}(S', a, w_T)} - \textcolor{blue}{\hat{q}(S, A, w)}) \nabla_w \textcolor{blue}{\hat{q}(S, A, w)}] \nonumber\]

• DNQ use the target network \(\textcolor{red}{\hat{q}(S', a, w_T)}\) and the main network \(\textcolor{blue}{\hat{q}(S, A, w)}\)
• \(w\) updates constantly while \(w_T\) updates periodically

Experience Replay

1. Collect experience samples \((s, a, r, s')\)
2. Store experience samples in a set, replay buffer, \(\mathcal{B} := {(s, a, r, s')}\)
3. Draw (called Experience Replay) a mini-batch from \(\mathcal{B}\) following a Uniform Distribution

Advantages

• Break the correlation between consequent samples
• Sample efficient

Policy Function Approximation

Value-based to Policy-based (Policy Gradient). PG algorithms are naturally On-Policy, but can be converted to Off-Policy.

\[\pi(a \vert s, \theta), \theta \in \mathbb{R}^{m}\]

• Also written as \(\pi_{\theta}(a \vert s)\) or \(\pi_{\theta}(a, s)\)

Metrics of Optimal Policy

Average (State) Value

\[\bar{v}_{\pi} = \sum_{s \in S} d(s) v_{\pi}(s)\]

• \(\displaystyle \sum_{s \in S} d(s) = 1 \Longrightarrow \bar{v}_{\pi} = \mathbb{E}[v_{\pi}(S)]\), where \(S \sim d\)
• \(\bar{v}_{\pi} = \mathbb{E}[v_{\pi}(S)] = d^T v_{\pi}\), where \(v_{\pi} = [\cdots, v_{\pi}(s), \cdots]^T \in \mathbb{R}^{\vert \mathcal{S} \vert}\) and \(d = [\cdots, d(s), \cdots]^T \in \mathbb{R}^{\vert \mathcal{S} \vert}\)
• \(d(s)\) can be either policy-dependent, such as when using the stationary distribution (\(d_{\pi}^T P_{\pi} = d_{\pi}^T\)), or policy-independent, such as when utilizing a uniform distribution.

Average (One-Step) Reward

\[\begin{align} \bar{r}_{\pi} = \sum_{s \in \mathcal{S}} d_{\pi}(s) \textcolor{red}{r_{\pi}(s)} &= \mathbb{E} [r_{\pi}(S)], S \sim d_{\pi} \nonumber\\ r_{\pi}(s) &= \sum_{a \in \mathcal{A}} \pi(a \vert s) \textcolor{red}{r(s, a)} \nonumber\\ r(s, a) &= \mathbb{E}[R \vert s, a] = \sum_{r} r p(r \vert s, a) \nonumber \end{align}\]

• \(d_{\pi}(s)\) is the stationary distribution
• \(\bar{v}_{\pi} = (1 - \gamma)\bar{r}_{\pi}\)

Equivalent Expressions

Metric	Expression 1	Expression 2	Expression 3
\(\bar{v}_{\pi}\)	\(\displaystyle \sum_{s \in S} d(s) v_{\pi}(s)\)	\(\mathbb{E}[v_{\pi}(S)]\)	\(\displaystyle \lim_{n\to\infty} \mathbb{E}[\sum_{t=0}^{n}\gamma^t R_{t+1}]\)
\(\bar{r}_{\pi}\)	\(\displaystyle \sum_{s \in S} d_{\pi}(s) r_{\pi}(s)\)	\(\mathbb{E}[r_{\pi}(S)]\)	\(\displaystyle \textcolor{red}{\lim_{n\to\infty} \frac{1}{n}\mathbb{E}[\sum_{t=0}^{n-1} R_{t+1}]}\)

Gradient of Metrics

\[\nabla_{\theta} J(\theta) \textcolor{red}{=} \sum_{s \in \mathcal{S}} \eta(s) \sum_{a \in \mathcal{A}} \nabla_{\theta} \pi (a \vert s, \theta) q_{\pi} (s, a)\]

• \(J(\theta)\) can be \(\bar{v}_{\pi}, \bar{v}_{\pi}^{0}\), or \(\bar{r}_{\pi}\)
• \(\textcolor{red}{=}\) can be \(=, \approx, \propto\)
• \(\eta\) is a distribution or weight of the states

\[\begin{align} \nabla_{\theta} J(\theta) &\textcolor{red}{=} \sum_{s \in \mathcal{S}} \eta(s) \sum_{a \in \mathcal{A}} \nabla_{\theta} \pi (a \vert s, \theta) q_{\pi} (s, a) \nonumber\\ &\textcolor{red}{=} \sum_{s \in \mathcal{S}}d(s) \sum_{a \in \mathcal{A}} \pi(a \vert s, \theta) \nabla_{\theta} \ln \pi(a \vert s, \theta)q_{\pi}(s, a) \Longleftarrow \nonumber\\ &\textcolor{red}{=} \mathbb{E}_{S \sim d} [\sum_{a \in \mathcal{A}} \pi(a \vert s, \theta) \nabla_{\theta} \ln \pi(a \vert S, \theta) q_{\pi}(S, a)] \nonumber\\ &\textcolor{red}{=} \mathbb{E}_{S \sim d, A \sim \pi} [\nabla_{\theta} \ln \pi(A \vert S, \theta) q_{\pi}(S, A)] \nonumber\\ &\approx \nabla_{\theta} \ln \pi(a \vert s, \theta) q_{\pi}(s, a) \nonumber \end{align}\]

• \(\Longleftarrow\) used \(\displaystyle \nabla \ln f(x) = \frac{\nabla f(x)}{f(x)}\)
• In order to calculate \(\ln \pi(a \vert s, \theta)\), use softmax function \(\displaystyle z_i = \frac{e^{x_i}}{\sum_{j=1}^n e^{x_j}}\) to normalize the entries from \((-\infty, +\infty)\) to \((0, 1)\)
• The policy function takes the form \(\displaystyle \pi(a \vert s, \theta) = \frac{e^{h(s, a, \theta)}}{\sum_{a' \in \mathcal{A}}e^{h(s, a', \theta)}}\)
• \(\pi(a \vert s, \theta) > 0, \forall a\), the parameterized policy is stochastic and hence exploreatory

Gradient Ascent

\[\begin{align} \theta_{t+1} &= \theta_t + \alpha \nabla_{\theta}J(\theta) \nonumber\\ &= \theta_t + \alpha \textcolor{red}{\mathbb{E}}[\nabla_{\theta}\ln \pi(A \vert S, \theta_t)q_{\pi}(S, A)] \nonumber\\ &\approx \theta_t + \alpha \nabla_{\theta} \ln \pi(a_t \vert s_t, \theta) \textcolor{red}{q_{\pi}(s_t, a_t)} \nonumber\\ &\approx \theta_t + \alpha \nabla_{\theta} \ln \pi(a_t \vert s_t, \theta) \textcolor{green}{q_t(s_t, a_t)} \nonumber \end{align}\]

Use SG to approximate \(q_{\pi}(s_t, a_t)\)

• Sample \(S \sim d\), where \(d\) is a long-run behavior under \(\pi\) (typically trivial because it’s impossible in reality)
• Sample \(A \sim \pi(A \vert S, \theta)\), hence $ a_t $ should be sampled following \(\pi(\theta_t)\) at \(s_t \Longrightarrow\) (On-Policy)

Actor-Critic Methods

Acotr refers to Policy Update and Critic refers to Policy Evaluation or Value Estimation.

\[\theta_{t+1} = \theta_t + \alpha \nabla_{\theta} \ln \pi(a_t \vert s_t, \theta) \textcolor{green}{q_t(s_t, a_t)}\nonumber\]

• The algorithm updates \(\theta\), thus corresponds to Actor
• Evaluation of \(q_t(s_t, a_t)\) corresponds to Critic
  • With Monte Carlo method \(\Longrightarrow\) REINFORCE
  • With TD Learning \(\Longrightarrow\) Actor-Critic

Q Actor-Critic (QAC)

• Sarsa + Value Function Approximation

Advantage Actor-Critic (A2C)

Introduce a baseline to reduce variance.

\[\begin{align} \nabla_{\theta} J(\theta) &\textcolor{red}{=} \mathbb{E}_{S \sim d, A \sim \pi} [\nabla_{\theta} \ln \pi(A \vert S, \theta) q_{\pi}(S, A)] \nonumber\\ &\textcolor{red}{=} \mathbb{E}_{S \sim d, A \sim \pi} [\nabla_{\theta} \ln \pi(A \vert S, \theta) (q_{\pi}(S, A) - \textcolor{green}{b(S)})] \nonumber\\ &\textcolor{red}{=} \mathbb{E}_{S \sim d, A \sim \pi} [\nabla_{\theta} \ln \pi(A \vert S, \theta) \delta_{\pi}(S, A)] \nonumber\\ \theta_{t+1} &\textcolor{red}{=} \theta_t + \alpha \nabla_{\pi} \ln \pi(a_t \vert s_t, \theta_t) \delta_t(s_t, a_t) \nonumber \end{align}\]

• The optimal baseline \(\displaystyle b^*(s) = \frac{\mathbb{E}_{A \sim \pi}[ \vert \vert \nabla_{\theta} \ln \pi(A \vert s, \theta) \vert \vert^2 q(s, A)]}{\mathbb{E}_{A \sim \pi}[ \vert \vert \nabla_{\theta} \ln \pi(A \vert s, \theta) \vert \vert^2]}\) is too complex, so it’s more often to use \(\displaystyle b^*(s) = \mathbb{E}_{A \sim \pi} [q(s, A)] = \textcolor{red}{v_{\pi}(s)}\)
• Advantage function: \(\delta_{\pi}(S, A) := q_{\pi}(S, A) - v_{\pi}(S)\)
• Use TD error to approximate \(\delta_{\pi}(s_t, a_t) := q_{\pi}(s_t, a_t) - v_{\pi}(s_t) = r_{t+1} + \gamma v_t(s_{t+1}) - v_t(s_t)\) so we can use only one network to approximate v_{\pi}(s)

Off-Policy AC

Importance Sampling
\(\begin{align} \mathbb{E}_{X \sim p_0}[X] &= \sum_{x} p_0(x) x = \sum_{x} p_1(x) \underbrace{\frac{p_0(x)}{p_1(x)}}_{f(x)} = \mathbb{E}_{X \sim p_1}[f(X)] \nonumber\\ &\approx \bar{f} = \frac{1}{n}\sum_{i=1}^{n}f(x_i) = \frac{1}{n}\sum_{i=1}^{n}\frac{p_0(x_i)}{p_1(x_i)} x_i \nonumber \end{align}\)

• Importance weight: \(\displaystyle \frac{p_0(x_i)}{p_1(x_i)}\)
• Importance Sampling is not limited to AC algorithms

\[\begin{align} \nabla_{\theta} J(\theta) &= \mathbb{E}_{S \sim d, \textcolor{red}{A \sim \beta}} [\textcolor{red}{\frac{\pi(A \vert S, \theta)}{\beta(A \vert S)}} \nabla_{\theta} \ln \pi(A \vert S, \theta) q_{\pi}(S, A)] \nonumber\\ \end{align}\]

Deterministic AC (DPG)

Stochastic policies cannot handle continuous (infinity) actions at time \(t\).

Deterministic Policy is denoted as \(a = \mu (s, \theta) := \mu(s)\)

• \(\mu: \mathcal{S} \sim \mathcal{A}\) is a mapping from states to actions

\[\begin{align} J(\theta) &= \mathbb{E}[v_{\mu}(s)] = \sum_{s \in \mathcal{S}}d_0(s)v_{\mu}(s) \nonumber\\ &= \sum_{s \in \mathcal{S}} \rho_{\mu}(s) \nabla_{\theta}\mu(s)(\nabla_a q_{\mu}(s, a)) \vert_{a=\mu(s)} \nonumber\\ &= \mathbb{E}_{\mathcal{S} \sim \rho_{\mu}}[\nabla_{\theta}\mu(s)(\nabla_a q_{\mu}(S, a)) \vert_{a=\mu(S)}] \nonumber\\ \theta_{t+1} &= \theta_t + \alpha_{\theta} \nabla_{\theta} \mu(s_t) (\nabla_a q_{\mu}(s_t, a)) \vert_{a=\mu(s_t)} \nonumber \end{align}\]

• Off-Policy. The behavior policy \(\beta\) can be different from \(\mu\)
• \(\beta\) can be replaced by \(\mu\) + noise so the algorithm can explore
• \(q(s, a, w)\) can be a Linuear Function (DPG) or Neural Networks (DDPG)

Miscellaneous

Mathematical Notation

• Approximately equal to: \(\doteq\) or \(\approx\).
• Defined as: \(:=\)