ECSE 506: Stochastic Control and Decision Theory

Aditya Mahajan
Winter 2022

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Prelim: Stochastic approximation

Suppose we have a function \(f \colon \reals^d \to \reals^d\) is such that the equation \[ f(θ) = 0 \] has a unique root \(θ = θ^*\). There are many methods for determining the value of \(θ\) by successive approximation where start with an initial guess \(θ_0\) and then recursively obtain a new value \(θ_{k+1}\) as a function of the previously obtained \(θ_0, \dots, θ_{k}\), the values \(f(θ_1), \dots, f(θ_{k})\), and possibly those of the derivatives \(f'(θ_0), \dots, f'(θ_{k})\), etc. If \[ \lim_{k \to ∞} θ_k = θ^*, \] irrespective of the initial condition \(θ_0\), then the successive approximation method is effective.

In many applications, the function \(f\) may be unknown so it is not possible to obtain the value \(f(θ)\), but it may be possible to conduct an experiment to get the value of \(f(θ)\) with noise. Stochastic approximation refers to recursive algorithms of the form \[ \begin{equation} \label{eq:SA} θ_{k+1} = θ_k + a_k[ f(x_k) + W_{k+1} ], \quad k \ge 0 \end{equation} \] where \(\{a_k\}_{k \ge 0}\) is a sequence of positive numbers and the noise \(\{W_k\}_{k \ge 0}\) is uncorrelated with zero mean.

The key idea behind stochastic approximation is that under appropriate conditions, the iteration \eqref{eq:SA} almost surely converges to the equilibrium point of the ODE \[ \begin{equation} \label{eq:ODE} \dot θ(t) = f(θ(t)) \end{equation} \] with initial conditions \(θ(0) = θ_1\).

In this section, we summarize these conditions (without proofs).

Suppose the following conditions are satisfied:

  1. Conditions on the function \(f\):

    1. The map \(f \colon \reals^d \to \reals^d\) is Lipschitz: \(\| f(θ_1) - f(θ_2) \| \le L \| θ_1 - θ_2 \|\) for some \(L \in (0, ∞)\).

    2. For any \(r \in \reals_{> 0}\), define \(f_r(θ) = f(rθ)/r\). There exists a function \(f_∞ \colon \reals^d \to \reals^d\) such that \[ \lim_{r \to ∞} f_r(θ) = f_∞(θ), \quad \forall θ \in \reals^d. \]

    3. Origin is the asymptotically stable equilibrium of the ODE \[ \dot x(t) = f_∞(x(t)). \]

  2. Condition on the noise \(\{W_k\}_{k \ge 0}\):

    1. \(\{W_k\}_{k \ge 0}\) is a martingale difference sequence with respect to the increasing family of \(σ\)-fields \[ \mathcal F_k = σ(θ_{1:k}, W_{1:k}). \] That is, \[ \EXP[ W_k | \mathcal F_k ] = 0, \text{ a.s.}, \quad k \ge 0. \]

    2. Furthermore, \(\{W_k\}_{k \ge 0}\) are square integrable with \[ \EXP[ \| W_{k+1}\|^2 | \mathcal F_k ] \le C_0(1 + \|θ_k\|^2) \text{ a.s.}, \quad k \ge 0\] for some constant \(C_0 \ge 0\) and any initial condition \(θ_0\).

  3. Condition on the step size \(\{a_k\}_{k \ge 0}\):

    The sequence \(\{a_k\}_{k \ge 0}\) is deterministic and assumed to satisfy one of the two following conditions:

    1. Tapering step sizes (TS): \(a_k \in (0, 1)\), and \[ \sum_{k=1}^∞ a_k = ∞, \qquad \sum_{k=1}^∞ a_k^2 < ∞. \]

    2. Bounded step sizes (BS): For some constants \(\underline a, \bar a \in (0, 1)\), we have \[ \underline a \le a_k \le \bar a, \quad k \ge 0. \]

Then, we have the following results. The first is under the (TS) condition on the step sizes.

Theorem 1

Assume conditions (1), (2), and (TS) hold. Then for any initial condition \(θ_0 \in \reals^d\), we have:

  • Asymptotic stability: \[ \sup_{k} \| θ_k \| < ∞, \text{ a.s.}. \]

  • Convergence: If the ODE \eqref{eq:ODE} has a unique globally asymptotically stable equilibrium point \(θ^*\). Then, \[ \lim_{k \to ∞} θ_k = θ^*, \text{ a.s.} \]

The second is under the (BS) conditions on the step sizes.

Theorem 2

Assume conditions (1), (2), and (BS) hold. In addition, the ODE \eqref{eq:ODE} has a unique globally asymptotically stable equilibrium point \(θ^*\). Then for any initial condition \(θ_0 \in \reals^d\), we have:

  • Asymptotic stability: There exists a \(a^* > 0\) and \(C_1 < ∞\) such that for
    all \(\bar a \in (0, a^*)\) and \(θ_0 \in \reals^d\), \[ \limsup_{n \to ∞} \EXP[ \| θ_k\|^2 ] \le C_1. \]

  • Convergence in probability: For \(\bar a \le a^*\) and any \(ε > 0\), there exists a \(b_1 = b_1(ε) < ∞\) such that \[ \limsup_{n \to ∞} \PR( \| θ_k - θ^* \| \ge ε ) \le b_1 \bar a. \]

  • Mean square convergence: If \(θ^*\) is exponentially asymptotically stable equilibrium point of the ODE \eqref{eq:ODE}, then there exists a \(b_2 < ∞\) such that for every initial condition \(θ_0 \in \reals^d\), \[ \limsup_{n \to ∞} \EXP[ \| θ_k - θ^*\|^2 ] \le b_2 \bar a. \]


The stochastic approximation algorithm was introduced by Robbins and Monro (1951). The results presented above are from Borkar and Meyn (2000), which also provides rates of convergence of stochastic approximation under stronger assumptions.

Borkar, V.S. and Meyn, S.P. 2000. The o.d.e. Method for convergence of stochastic approximation and reinforcement learning. SIAM Journal on Control and Optimization 38, 2, 447–469. DOI: 10.1137/s0363012997331639.
Robbins, H. and Monro, S. 1951. A stochastic approximation method. The Annals of Mathematical Statistics 22, 3, 400–407. DOI: 10.1214/aoms/1177729586.

This entry was last updated on 13 Jul 2021