# OpenSpiel

Google DeepMind · C++ core with Python bindings · Apache-2.0 `unverified` ·
`checked` for scope and algorithms

## What it optimises for

**Research in reinforcement learning and search/planning in games**, with
a strong game-theoretic algorithm library.

Supports n-player single- and multi-agent, **zero-sum, cooperative and
general-sum**, one-shot and sequential, turn-taking **and
simultaneous-move**, perfect and imperfect information — plus grid worlds
and **social dilemmas**. `checked`

## How a game is defined

Games are implemented against a C++ (or Python) API. `checked` — this is
a *programming* interface, not a description language, which is the axis
where Ludii and Ai Ai differ from it.

**But the API is an extensive-form game interface**, and that is the
connection: Ludii's universality proof grounds its language in EFGs, and
OpenSpiel's algorithms consume EFGs. **EFG is the interchange format**
([`../research/CB-RES-0009-extensive-form-is-the-lingua-franca.md`](../research/CB-RES-0009-extensive-form-is-the-lingua-franca.md)).

## What it can analyse

The reason this profile matters for Track B. Implemented algorithms
include **CFR** and its Monte-Carlo variants, **Deep CFR**, **exploitability**
and best-response computation, fictitious play (XFP, NFSP), **PSRO**,
**α-Rank**, replicator/evolutionary dynamics, minimax, MCTS, and
value iteration. `checked`

**Exploitability** is the key one: how much an opponent gains by
deviating to a best response — a direct, computable measure of *how far
from equilibrium a strategy is*. `checked`

## What it does not do

- Nothing design-facing. It answers "how strong is this strategy" and
  "how far from equilibrium", not "is this rule doing its job".
- No game description language, so no cheap path for a designer.
- No evidence discipline: outputs are research data.

## Relevance to clay-borg

**Track B, and it is very likely the answer rather than a competitor.**

Our current instrument for "does ATTACK ever pay" is two hand-written
policies and a rank parameter. **Exploitability and best-response are the
principled versions of that question**, and they are implemented,
tested and published here.

GROUND is simultaneous-move, imperfect-information, cooperative with a
defection mechanic — **all four are inside OpenSpiel's stated scope**.

## What to steal

- **Exploitability as the replacement for "we tried three policies".**
  F17's question — does ATTACK earn its place — is a best-response
  question wearing a sweep's clothes.
- The distinction between **cooperative, general-sum and zero-sum** as a
  first-class property of a game, which our kernel does not represent.

## What to avoid

**Quoting a solution concept without its assumptions.** An equilibrium
computed over a policy class we chose is a statement about that class.
This is the wrong-subject error in mathematical dress, and it is the
specific risk `Positioning.md` §4 flags for Track B.

## Open questions

- Cost of expressing a GROUND-like game against its API versus our kernel.
- **Whether our engine satisfies perfect recall.** CFR and exploitability
  assume it, our per-seat projection has never been checked for it, and
  it is checkable from the journal. **This is the first Track B task** —
  if it fails, every equilibrium concept is unsound here.
- **Our chance is folded into a seed, not an explicit chance player.** A
  clay-borg game is one chance realisation; the panels sample seeds to
  approximate the distribution. CFR needs chance nodes.
- Whether exploitability is meaningful for a co-operative game with a
  shared threshold — `open`, and it is the question that decides whether
  Track B adopts this or only borrows its vocabulary.
- Licence.

## Sources

- OpenSpiel repository (https://github.com/google-deepmind/open_spiel)
- *OpenSpiel: A Framework for Reinforcement Learning in Games*
  (https://www.researchgate.net/publication/335419770)