Abstract
Intrinsic motivation, inspired by the psychology of developmental learning in infants, stimulates exploration in agents without relying solely on sparse external rewards. Existing methods in reinforcement learning like Random Network Distillation (RND) face significant limitations, including (1) relying on raw visual inputs, leading to a lack of meaningful representations, (2) the inability to build a robust latent space, (3) poor target network initialization and (4) rapid degradation of intrinsic rewards. In this paper, we introduce Pre-trained Network Distillation (PreND), a novel approach to enhance intrinsic motivation in reinforcement learning (RL) by improving upon the widely used prediction-based method, RND. PreND addresses these challenges by incorporating pre-trained representation models into both the target and predictor networks, resulting in more meaningful and stable intrinsic rewards, while enhancing the representation learned by the model. We also tried simple but eff