April 2026 - OpenHUTB/nn monthly performance summary. Major work: - End-to-End Autonomous Driving with TORCS using DDPG: established an end-to-end autonomous driving pipeline in the TORCS simulator, including neural network architectures and environment setup. The work also included project scaffolding and repository refactoring to accommodate TORCS/CARLA end-to-end driving assets (commit 0cde7a9004eadbd7b156d5c6fbdea97dee91711d). - TD3-Based Autonomous Agent Training in CarRacing-v3: complete TD3 implementation for CarRacing-v3, with environment wrappers, TD3 model definitions, and a runnable training script (commit 2d041e970d7ed178a163db6140e3a91739a42fda). - Enhanced Vehicle Control in CarRacing: significant stability and performance improvements, including action smoothing via SmoothActionWrapper and EMA filtering, anti-spin penalties, track detection, boundary constraints, and reward shaping to drive on-track behavior (commits be55fa9a5850768a282eb4d86a91729086aa69c4; 6706cc5800f11a1eb484de3a77e4aa5b352b5e73; fb17ee98d5d6c3ae314f5175790413d71723679b; 224422d79f139e124acc155aaa63886292d32c61; bf6b91da25a3748de37c0224836d05259fb022b7). Impact: - Accelerated prototyping for autonomous driving research in simulated environments with clear pipelines for TORCS and CarRacing. - Improved training stability and learning efficiency through action smoothing, reward shaping, and boundary controls, enabling more reliable policy convergence. - Better code quality and documentation supporting collaboration and reproducibility across RL experiments. Technologies/Skills demonstrated: - Reinforcement learning algorithms: DDPG, TD3 - Simulation environments: TORCS, CarRacing-v3 - Software design: environment wrappers, model architectures, training pipelines, action smoothing, track/boundary detection, reward shaping - Code quality: documentation, refactoring, and commit hygiene