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Build the game as a deterministic simulation first, then add the browser as its renderer and the AI as a constrained director. A run should advance only when the rules layer receives an explicit action and controlled random input—not because a browser frame happened or a model returned an unpredictable result. Record the seed and action history so you can replay runs and compare behavior after code changes.
How do you make a browser roguelite deterministic?
Represent the authoritative game state explicitly and advance it through a stable transition: current state plus an action and controlled random state produces the next state. Keep this rules simulation independent of rendering, browser timing, and ambient sources of randomness. That separation lets the browser display the game without deciding what happens in it.
Use a seeded random-number generator for procedural maps, encounters, loot, and other randomized rules. Record the seed and the ordered action sequence for each run. Given the same initial state, seed, rules version, and inputs, the simulation can then be replayed and its output compared. This is a project architecture to implement and test, not a guarantee that every browser and machine will produce bit-for-bit identical results.
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The Hayao project describes its game as “a pure function of its inputs,” with randomness flowing through a seeded generator and no wall-clock or hidden state in the simulation (Hayao project website). That is a useful design model, not an independently validated standard.
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Keep browser timing out of game rules
The browser should capture input and render the latest state. Do not make outcomes depend on frame rate, timers, or how long rendering takes. A fixed timestep is one documented approach to repeatable simulation; the Open Terrain Engine vision page discusses it, but also says browser support is outside its v1 scope and the engine is still in development (Open Terrain Engine vision). Treat that page as design context, not as evidence of a browser-ready engine.
Model the roguelite as explicit state
Make run progression, map generation, combat, and rewards part of the state transition rather than side effects scattered through UI code. The roguelite-core README documents seeded procedural maps, run reducers, and batch simulation as implementation patterns. Those project claims illustrate an approach; they do not establish how balanced or enjoyable a new game will be.
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How can an AI direct the game without changing its rules unpredictably?
Give the director a narrow, documented observation-and-action contract. It can inspect structured facts—such as player resources, current room, recent events, or available encounter slots—and propose a level modifier, encounter, narrative beat, or legal game action. The deterministic rules layer must validate the proposal and decide whether and how it takes effect.
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Do not let an unconstrained model call resolve authoritative gameplay directly. Model output can vary, arrive late, or be malformed; treat it as a proposal, not as trusted state. Record accepted proposals as explicit inputs if they affect the run, so a replay can use the same decisions without needing to reproduce a model response.
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This boundary also makes failures easier to handle: reject invalid proposals, apply defined fallbacks, and keep the simulation’s transition rules responsible for all state changes. The AI may shape what happens next, but it should not silently rewrite the rules that determine what those choices do.
Should the simulation run in the browser or on a server?
There is no universally correct location. For a single-player prototype, running the authoritative simulation in the browser can simplify deployment and enable local headless reuse. A server-authoritative design can be more appropriate when multiplayer, security, or centralized control matters. In either arrangement, keep the rules module usable without rendering so the same simulation can be exercised in automated runs.
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Dino Arena describes one simulation module used by a browser loop and a Node benchmark harness. The Dinoz README describes a server-authoritative simulation with a browser client that renders snapshots and submits actions. These are examples of the pattern, not a controlled comparison or a recommendation for every game.
How do you replay runs and catch regressions?
- Store the replay inputs. Save the initial seed and each accepted player or director action in order. Include any other explicit inputs that can affect the result.
- Run the same rules headlessly. Use the game logic without browser rendering to execute fixed seed-and-action suites. This makes it practical to test many runs without playing each one manually.
- Compare outcomes after a change. Replay known inputs against the revised build and compare final state or intermediate state hashes. A difference identifies a changed simulation result that can be investigated; it does not by itself prove the change is a bug.
- Inspect content and balance separately. Headless batches can help find unwinnable maps, regressions, or shifts in outcomes across a seed suite. They provide evidence for balance work, not a result about whether the game is fun or fair for players.
- Validate supported runtimes. Test the browsers and runtimes the project intends to support, and keep nondeterministic APIs out of authoritative game rules. Deterministic inputs improve repeatability but do not prove cross-device bitwise identity.
Hayao documents replay and hash comparison as ways to detect changes in simulation output (Hayao). The roguelite-core project documents batch simulation (roguelite-core). These techniques make repeatable testing possible; the project still has to define meaningful checks and interpret the results.
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What determinism does—and does not—tell you
A deterministic baseline gives developers a reliable way to reproduce a run, test the same inputs after a code change, and separate simulation behavior from browser presentation. It does not establish that the game is balanced, compelling, or compatible bit for bit across all machines. Those are properties to evaluate in the actual project with appropriate playtesting and runtime checks.
The OpenGame paper abstract notes that code agents still struggle to produce fully playable games from high-level designs and describes headless-browser execution in its evaluation pipeline (OpenGame: Open Agentic Coding for Games). That is context for including build and playability checks in a development workflow; it is not evidence that an AI director will generate good content.
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