🚀 Breaking the Exploration-Exploitation Dilemma in Dynamic Environments
Developing an AI agent that effectively navigates complex, dynamic environments with non-linear reward functions and sparse feedback is a long-standing challenge in artificial intelligence. To tackle this problem, we propose integrating a Novelty-Based Exploration algorithm with a Hierarchical Temporal Memory (HTM).
Novelty-Based Exploration is an algorithm that encourages the agent to explore the environment by rewarding it for discovering new, novel experiences. This approach promotes efficient exploration, as the agent is motivated to seek out novel and interesting experiences, rather than simply exploiting known rewards.
Hierarchical Temporal Memory (HTM) is a neural network architecture that mimics the structure and function of the brain's temporal lobe. It's particularly well-suited for processing sequential data and recognizing patterns in time-series data. HTMs are capable of learn...
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