AI Plays Resident Evil with BC + HG-DAgger

Enhanced Key Takeaways

• •The project utilizes a specific implementation of HG-DAgger (Hybrid Generative Data Aggregation) designed to mitigate the 'covariate shift' problem inherent in standard Behavior Cloning, where small errors in early navigation compound over time.
• •The agent architecture leverages a lightweight CNN-based feature extractor to process raw frame buffers, which are then fed into a recurrent policy network to maintain temporal context necessary for Resident Evil's dynamic, non-Markovian environment.
• •The training pipeline incorporates a 'safety-critical' replay buffer that prioritizes frames where the agent's predicted action deviates significantly from the human expert's recorded trajectory, specifically targeting high-stakes combat and evasion scenarios.

Technical Deep Dive

• •Policy Architecture: Uses a Recurrent Neural Network (RNN) or LSTM layer to handle the partially observable nature of the game environment.
• •Action Space: Discretized controller inputs (D-pad directions, action buttons) mapped to a multi-categorical distribution output.
• •Data Aggregation: HG-DAgger implementation involves an iterative process where the agent is deployed in the environment, and a human expert (or a heuristic oracle) provides corrective labels for states where the agent's policy diverges.
• •Preprocessing: Frame stacking (typically 4 frames) to capture motion information, followed by grayscale conversion and downsampling to reduce input dimensionality.

Future ImplicationsAI analysis grounded in cited sources