Hybrid Deep Learning and Reinforcement Learning Approach for Autonomous Decision-Making in Dynamic Environments

Abstract

Autonomous decision-making in dynamic, uncertain environments (such as robotic navigation, self-driving vehicles, and smart grid management) requires integrating powerful perception with adaptive planning. We propose a hybrid framework that combines deep learning for perceptual representation and reinforcement learning (RL) for sequential control. The framework uses convolutional or recurrent neural networks to process high-dimensional sensory data (e.g., camera or sensor arrays) and outputs features or latent states, which are then fed into an RL algorithm (such as Deep Q-Network or Actor-Critic) to learn optimal policies. We detail the architecture, including neural-network-based value/policy approximation and experience replay mechanisms, and describe our simulated testbed (e.g. CARLA urban driving simulator or OpenAI Gym environments). Experiments in a simulated dynamic scenario demonstrate that our hybrid approach significantly outperforms standard tabular RL and pure deep-learning baselines. We provide training curves, comparative tables of performance metrics, and discuss the implications for real-world deployment. Key contributions include: a conceptual integration of deep representation learning with RL for dynamic control; a detailed methodology for training in simulation; empirical results illustrating learning progress and robustness.