Target-Driven Exploration: Intrinsic Rewards for Sparse Reward Problems

Sparse reward environments present a significant challenge in reinforcement learning (RL) due to the limited feedback from extrinsic rewards. This research proposes a target-driven exploration framework that leverages intrinsic rewards derived from self-supervised feature extraction to guide agents toward meaningful states. The framework employs three feature extraction methods—Raw Pixels, Random Fixed Features, and Variational Autoencoders (VAEs)—to represent state embeddings and compute intrinsic motivation. Evaluations on benchmark environments, such as SpaceInvaders, demonstrate improved exploration efficiency and learning capabilities. The results highlight the framework's ability to utilize intrinsic rewards effectively, enabling agents to navigate and learn in complex, sparse reward settings. Future work will refine feature extraction techniques and further optimize the model to address more complex environments.