Accurate GNU Vs Local Backgammon Game Orchestrator Simulation

by Dimemap Team 62 views

Hey everyone! Let's dive into a detailed discussion about simulating accurate backgammon games, specifically focusing on GNU Backgammon versus a Local Game Orchestrator. This is super important for ensuring our AI plays as smartly as possible, so stick around!

Summary

In our quest for backgammon brilliance, we've noticed that while GNUBG (GNU Backgammon) provides some seriously authoritative move sequences, our CORE system sometimes struggles to execute the planned second sub-move, especially when dealing with the remaining die. This can lead to fallbacks, also known as "overrides," which can weaken the robot's gameplay. The primary goal here is to develop a simulation that mirrors real-world gameplay as closely as possible. This means using the same AI and CORE pathways that are used in live play. Think of it as creating a virtual backgammon lab where we can fine-tune everything without affecting the real game.

This initiative aims to create a faithful simulation that replicates live play conditions. The current system sometimes struggles with executing the second sub-move, leading to less-than-optimal gameplay. By creating this simulation, we can identify and rectify these issues, ensuring smoother and more accurate gameplay.

Goal

The main goal is crystal clear: simulate full backgammon games using the exact same production paths our live game uses. This means:

  • Simulate full games using production paths: We need to run entire games from start to finish in our simulation environment.
  • Zero overrides (planner respected 100%): We want our simulation to execute moves exactly as planned, without any fallbacks or overrides. This is crucial for accurate testing.
  • Abort and log context on mismatches; feed into CORE tests and patches: If any discrepancies occur between the planned move and the executed move, we need to immediately stop the simulation, log all relevant details, and use this information to improve our CORE system. Think of it as having a detective on the case, ready to gather clues when something goes wrong.

To ensure the integrity of the simulation, the objective is to achieve zero overrides, meaning the planner's decisions are executed flawlessly every time. Any deviation from the planned move should trigger an immediate halt, with detailed logs generated to pinpoint the cause. This data will be invaluable for refining CORE tests and applying necessary patches, ultimately enhancing the reliability of the system.

Principles

To achieve our goal, we're sticking to a few key principles:

  • Use the same code path as live play: No shortcuts! We need to use the exact same code that runs in our live game to ensure the simulation is as accurate as possible.
  • Let AI (GNUAIProvider) plan and execute GNU turns end-to-end: We're putting our AI, specifically the GNUAIProvider, in charge of planning and executing GNU's turns from start to finish. This includes one-shot planning, robust mapping, bounded rehinting, and handling die alignment—all within the AI.
  • Execute moves via CORE (Game.executeAndRecalculate + confirmTurn): All moves, whether planned by the AI or chosen by a local opponent, must be executed through our CORE system using Game.executeAndRecalculate() and confirmTurn(). This ensures consistency and accuracy.
  • Record structured telemetry; never guess: We're not leaving anything to chance. We'll record detailed telemetry for every game, capturing all the important data points. No guessing allowed!

Adhering to these principles ensures the simulation closely mirrors the live environment, providing a reliable platform for testing and improvement. By using the same code paths, leveraging the GNUAIProvider for AI turns, and executing moves via CORE, we maintain consistency and accuracy. Structured telemetry is paramount, enabling thorough analysis and preventing assumptions.

Design

Our simulation design revolves around an orchestrator loop that manages the game flow. Here's how it works:

  1. Initialize game via CORE: We start by setting up the game board and initial state using our CORE system.
  2. While not completed: We keep the game running until someone wins or the game ends.
    • If active player is GNU: We call GNUAIProvider.executeRobotTurn(game) to let the AI plan and execute the turn. The returned state becomes the new game state. This includes handling the entire turn in one shot, robust move mapping, bounded rehinting, and die alignment.
    • Else (local opponent): If the active player is a local opponent, we choose a move from activePlay.moves[].possibleMoves[] and execute it using Game.executeAndRecalculate(). This allows us to simulate human players or use different AI strategies.
    • After turn: We use Game.checkAndCompleteTurn() and confirmTurn() to transition to the next turn, ensuring all game rules are followed.
    • If AI telemetry indicates usedFallback===true or override.reasonCode (e.g., core-move-mismatch), stop and dump diagnostics: This is our safety net. If the AI telemetry indicates any issues, such as a fallback or a mismatch in move execution, we immediately stop the simulation and dump all diagnostic information. This helps us quickly identify and fix problems.

Our design incorporates an orchestrator loop to manage game flow effectively. This loop handles game initialization, AI and local player turns, and turn transitions. A critical aspect is the real-time monitoring of AI telemetry for fallbacks or mismatches. When these issues arise, the simulation halts, and detailed diagnostics are generated, enabling prompt investigation and resolution.

We've also got a couple of variants planned:

  • GNU vs GNU (benchmark): This is where we pit GNU against itself. Both sides use GNUAIProvider.executeRobotTurn() to play, allowing us to benchmark the AI's performance.
  • GNU vs Local: In this mode, GNU plays against a local opponent. The local side can either choose moves strictly from the possibleMoves list provided by CORE or use a different strategy or policy. This helps us test the AI against various playstyles.

The simulation includes variants like GNU vs GNU for benchmarking and GNU vs Local to test the AI against different playstyles. These variations offer comprehensive testing scenarios to ensure the robustness of the simulation.

Tasks

Alright, let's break down the tasks we need to tackle:

  • [ ] Implement simulation script (e.g., packages/core/src/scripts/simulateGnuVsLocal.ts).
    • Register AI provider before starting. This ensures our AI is ready to go.
    • GNU turns → GNUAIProvider.executeRobotTurn(). We use this method for all GNU turns.
    • Local turns → select from possibleMoves and call Game.executeAndRecalculate(). This allows local players to make their moves.
    • Always call Game.checkAndCompleteTurn() / confirmTurn. This is crucial for proper game state management.
    • Abort and log if any fallback occurs. This ensures we catch any errors during the simulation.
  • [ ] Add npm script aliases (e.g., simulate:gnu-vs-local, simulate:gnu-vs-gnu). These aliases will make it easier to run the simulations.
  • [ ] Integrate telemetry/diagnostics (reuse scripts/analysis/* and scripts/diagnostics/core-mismatch.log). We'll reuse existing scripts for analysis and diagnostics to save time and ensure consistency.

Key tasks include implementing the simulation script, adding npm script aliases for easy execution, and integrating telemetry and diagnostics to monitor and analyze the simulation runs. These steps are essential for building a functional and informative simulation environment.

Acceptance Criteria

How do we know we've succeeded? Here are our acceptance criteria:

  • Running GNU vs GNU and GNU vs Local yields full games without overrides. This is the big one! We need to run complete games without any fallbacks.
  • Batch runs (50+ games) show zero overrides via npm run verify:gnubg-zero-overrides. We'll run multiple games in a batch to ensure our simulation is robust and reliable.
  • Any mismatch logs context (planned step, direction, remaining die, READY sample) to reproduce and patch CORE. If a mismatch does occur, we need detailed logs to help us understand and fix the issue.

Success will be measured by the simulation's ability to run full games without overrides. Batch runs of 50+ games must demonstrate zero overrides, and any mismatches should generate comprehensive logs to facilitate debugging and patching.

Analysis & CI

To keep tabs on our progress, we'll use a few key reports:

  • npm run report:overrides -- --limit 50
  • npm run report:core-mismatches -- --limit 50
  • npm run verify:gnubg-zero-overrides -- --limit 50

We'll also set up a Continuous Integration (CI) job that runs the verifier. If the verifier fails, we'll block merges to ensure we don't introduce any regressions.

Analysis will be conducted using reports to track overrides and CORE mismatches. A CI job will be implemented to verify zero overrides, ensuring continuous monitoring and preventing regressions.

Risks / Mitigations

Let's talk about potential risks and how we'll handle them:

  • Directional regressions: To mitigate this, we'll use helpers and unit tests to guard invariants. Think of these as our safety nets to catch any unexpected behavior.
  • Hint surprises: We'll use bounded rehinting and logging to handle this. We'll also capture the HintRequest and PID when needed. It’s like having a detailed record of every hint given and requested.
  • Doubles complexity: We'll keep CORE doubles tests green and ensure no doubles changes are made without tests. This is crucial for maintaining the stability of our game logic.

Potential risks, such as directional regressions, hint surprises, and doubles complexity, are addressed with specific mitigations. Helpers and unit tests guard against regressions, bounded rehinting and logging handle hint surprises, and rigorous testing protocols ensure doubles complexity is managed effectively.

References

Here are some key references for those who want to dive deeper:

  • AI executor: packages/ai/src/robotExecution.ts
  • CORE enumeration: packages/core/src/Board/methods/getPossibleMoves.ts
  • CORE play sequencing: packages/core/src/Play/index.ts
  • Types (telemetry): packages/types/src/history.ts
  • Diagnostics/Reports: scripts/analysis/*.js

These references provide access to the core components and scripts involved in the simulation, enabling further exploration and understanding.

Alright guys, that's the rundown on our GNU vs Local Game Orchestrator simulation! It's a crucial step in making our backgammon AI even smarter, and I'm stoked to see the progress we make together. Let's keep the conversation rolling and make some serious strides in backgammon AI!