Addressing Exploration-Exploitation Trade-offs in Reinforcement Learning via Adaptive Intrinsic Reward Shaping

Reinforcement learning (RL) agents often struggle with sparse reward environments, leading to inefficient exploration and suboptimal policy convergence. This paper investigates a novel approach to mitigate this challenge by introducing an adaptive intrinsic reward shaping mechanism. Our method dynamically adjusts the intrinsic reward signal based on the agent's learning progress, balancing the exploration of novel states with the exploitation of known high-reward regions. We present a theoretical framework for this adaptive reward shaping and demonstrate its effectiveness through empirical evaluations on benchmark RL tasks.