Leveraging Multi-Resolution Learning in Reinforcement Environments: Implementing the Never Give Up (NGU) Algorithm

This paper presents an enhanced implementation of the Never Give Up (NGU) algorithm in multiresolution grid-based environments to address challenges in sparse reward reinforcement learning (RL). Sparse reward settings present significant challenges due to limited feedback signals, often resulting in inefficient exploration and learning stagnation. We propose a framework incorporating intrinsic reward mechanisms, episodic memory implementation, dynamic exploration strategy with epsilon decay, and adaptive state-space representation via a multi-resolution approach. Our experimental results demonstrate significant performance improvements over conventional Deep Q-Network (DQN) agents, achieving a 90.08% improvement in cumulative reward accumulation compared to Fixed Resolution DQN and 90.06% improvement compared to Standard DQN. However, the claimed improvements in goal completion time and unique state visitation could not be conclusively verified due to threshold sensitivity and lack of raw visitation data. The multi-resolution architecture enables enhanced adaptability across diverse task complexities, while the intrinsic reward mechanism facilitates exploration of previously unvisited states, promoting comprehensive environmental mapping. The episodic memory component optimizes training efficiency by minimizing redundant state explorations. Additionally, we propose future research directions including extension to continuous action spaces, integration of advanced reward shaping methodologies, and implementation in robotic systems and autonomous platforms. Our findings validate the efficacy of combining intrinsic motivation mechanisms with scalable state-space representations for addressing sparse reward challenges in RL applications, demonstrating quantitative superiority in exploration efficiency, learning convergence, and reward optimization.