My first instinct was to just hardcode some threshold on the bird's y-position relative to the pipe gap center, which technically works but falls apart on edge cases near the floor.
Start by clarifying the game state representation and constraints, then outline a forward simulation approach that predicts the bird's trajectory and pipe positions. Emphasize safety margins and a decision rule that balances survival with progress, and discuss trade-offs between simulation depth and computational cost.
Pro tip: Mention that you would simulate multiple candidate actions (jump vs. no jump) and choose the one that maximizes a safety score, but also include a fallback heuristic for edge cases like screen boundaries. This shows you think about robustness and real-time performance.
Define the agent's interface: what information is available each frame (bird position, velocity, pipe positions, screen dimensions) and what the method should return (a boolean jump decision).
Describe how to simulate the bird's future positions over a short horizon (e.g., next few frames) under both jump and no-jump actions, using simple physics (gravity, jump impulse).
Establish safety margins for pipe gaps and screen boundaries, and create a scoring function that penalizes proximity to obstacles and rewards staying centered in the gap.
Compare simulated outcomes for jump vs. no-jump, choose the action with the higher safety score, and include a tie-breaker or fallback heuristic (e.g., jump if below a threshold).
Address computational cost of simulation, potential for precomputation or caching, and how to tune parameters like simulation horizon and safety margins for different difficulty levels.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.