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Blackwell Approachability


Suppose that Alice and Bob is playing a so-called two-player zero-sum game with perfect information. Alice chooses her action from a finite action set ; Bob chooses his action from a finite action set . There is a payoff function $f$, which assigns to each action pair $(a, b) \in \mathcal{A} \times \mathcal{B}$ a payoff $f ( a, b ) \in \mathbb{R}$; Bob’s payoff is then simply $-f (a, b)$. In general, Alice and Bob may choose their actions randomly, so we consider the expected payoff: for any probability distribution $P \in \Delta ( \mathcal{A} )$ and $Q \in \Delta ( \mathcal{B} )$, define

If Alice does not have any additional information about Bob, it is reasonable to consider the very robust minimax strategy, i.e., to choose her action randomly according to

No matter what Bob’s strategy is, Alice is guaranteed to achieve at least the minimax expected payoff.

If the payoff function $f$ is vector-valued, talking about the minimax expected payoff becomes nonsense, as we cannot compare two vectors. This post introduces an analog for the vector-valued payoff case, proposed by David Blackwell in this classic paper.

Blackwell Approachability

In the rest of this post, suppose that the payoff function $f$ takes values in .

Blackwell considered the following protocol of repeated games. For $t = 1, 2, \ldots$,

  1. Alice announces $a_t \in \mathcal{A}$.
  2. Bob announces $b_t \in \mathcal{B}$, without known $a_t$.

In general, Alice can use a randomized strategy. For convenience, let us say that Alice chooses $a_t$ randomly according to some probability distribution .

Recall that Alice cannot achieve the “minimax expected payoff” in this vector-payoff case, as it is just not well-define. However, it might be possible to ensure that her average payoff will ultimately lies in some specific subset of $\mathbb{R}^d$.

Define the average payoff

Definition. Let $\mathcal{X} \subseteq \mathbb{R}^d$. The set $\mathcal{X}$ is said to be approachable by Alice, if for any sequence ,

where $\mathrm{dist} ( \bar{f}_T, \mathcal{X} )$ denotes the $2$-norm distance between $\bar{f}_T$ and the projection of $\bar{f}_T$ onto $\mathcal{X}$.

If we only consider convex sets, there is an exact characterization of approachable sets.

Theorem 1. A convex set is approachable to Alice, if and only if for any $Q \in \Delta ( \mathcal{B} )$, there exists some $P \in \Delta ( \mathcal{A} )$ such that $f ( P, Q ) \in \mathcal{X}$.

Exercise. Suppose that for each $t$, Bob’s action $b_t$ can be dependent on Alice’s action $a_t$. Show that then even when the if and only if condition holds, $\mathcal{X}$ may not be approachable to Alice.

The only if part of Theorem 1 is easy to check. Suppose that there exists some $Q^\star \in \Delta ( \mathcal{B} )$, such that for any . To guarantee that Alice fails to approach $\mathcal{X}$, Bob can always choose $b_t$ randomly according to $Q^\star$ for all $t$.

We will focus on the if part of Theorem 1 in the rest of this post.

Blackwell’s Condition and Alice’s Strategy

To prove the if part of Theorem 1, we need to show the existence of an approaching strategy. Indeed, the proof is constructive; there is an explicitly defined approaching strategy based on the following lemma.

Lemma 1. Suppose that for any $Q \in \Delta ( \mathcal{B} )$, there exists some $P \in \Delta ( \mathcal{A} )$ such that $f ( P, Q ) \in \mathcal{X}$. Then for any $v \in \mathbb{R}^d$, there exists some $P \in \Delta ( \mathcal{A} )$, such that

where $\mathrm{proj}$ denotes the projection (w.r.t. the $2$-norm) onto $\mathcal{X}$.

Proof. By definition, $\mathrm{proj} (v)$ is the minimizer of the $2$-norm distance to $v$ in $\mathcal{X}$. The optimality condition says

What we want to prove can be equivalently written as

Applying von Neumann’s minimax theorem, we can exchange the order of $\min$ and $\max$, and write

The lemma follows from the optimality condition. $Q.E.D.$

We call the condition in Lemma 1 Blackwell’s condition, following the convention in literature. Blackwell’s condition ensures that the following strategy for Alice is well-defined.

Alice’s Strategy. Define the expected and average expected payoff:

For each $t \in \mathbb{N}$, Alice computes some $P_t$ such that

and then announces $a_t$ randomly according to $P_t$.

Remark. In practice, $P_t$ can be computed as

This is a bilinear saddle point problem; there exists a variety of algorithms solving such a problem.

Proof of Theorem 1

Since $\mathcal{X}$ is approachable if and only if its closure is approachable, we assume that $\mathcal{X}$ is closed w.l.o.g. We will also assume that $\mathcal{X}$ is bounded for convenience; we will relax this unnecessary condition at the end of the proof.

Theorem 1 is in fact a corollary of the following theorem.

Theorem 2. Suppose that the if and only if condition in Theorem 1 holds. Suppose that Alice adopts the strategy described above. Then for each $T \in \mathbb{N}$ and $\delta \in ( 0, 1 )$, with probability at least , it holds that

where , and .

To prove Theorem 2, we start with the following lemma.

Lemma 2. For all $T \in \mathbb{N}$, it holds that

Proof. Define and . By definition, we have

Writing , we have

Applying Lemma 1 and the triangle inequality, we obtain

The lemma can be then easily checked by induction. Q.E.D.

Proof of Theorem 2. Recall that denotes the average payoff; define . We start with the decomposition,

By Lemma 2 and the non-expansiveness of projections, we have

Define . Then forms a vector-valued martingale. Applying the Azuma inequality for Banach-space valued martingales, we obtain, for any $u > 0$,

for some constant $C > 0$. Q.E.D.

In general, the set to be approached may not be bounded; then $R = + \infty$ if we directly apply the definition. Notice that, however, only sets lie in the convex hull of can be approached. Denote the convex hull by $\mathcal{F}$. Hence, it suffices to replace $\mathcal{X}$ by , which is always bounded by construction; then it is easily checked that $R$ can be bounded above by $M$ to slightly simplify the expressions.

Calibrated Forecasting

One might wonder why one should consider vector-valued payoffs. An interesting application is calibrated forecasting. With Theorem 1, it is an easy exercise to prove the theorem in the previous post, showing the existence of an $\varepsilon$-calibrated forecasting strategy.


The strategy of Alice presented above is not exactly the original one proposed by Blackwell, as the original one does not lead to the $O ( 1 / \sqrt{T} )$ with-high-probability convergence rate in Theorem 2. The strategy presented above was described in the book by Cesa-Bianchi and Lugosi, without proof. The proof above is basically a mix of the arguments and results in the following references.

  1. Blackwell, D., 1956. An analog of the minimax theorem for vector payoffs. Pac. J. Math.
  2. Cesa-Bianchi, N. and Lugosi, G., 2006. Prediction, Learning, and Games.
  3. Naor, A., 2012. On the Banach-space-valued Azuma inequality and small-set isoperimetry of Alon–Roichman graphs. Combinatorics Probab. Comput.
  4. Sorin, S., 2002. A First Course on Zero-Sum Repeated Games.