What's New in Deep Learning Research: Knowledge Exploration with Parameter Noise

The exploration vs. exploitation dilemma is one of the fundamental balances in deep reinforcement learning applications. How much resources to devote to acquire knowledge that can improve future actions versus performing specific actions? This is one of the main heuristics that rule the behavior of reinforcement learning systems. In theory, optimal exploration should always conduce to more efficient knowledge but this is far from true in the real world. Developing techniques to improve the exploration of an environment is one of the pivotal challenge of the current generation of deep reinforcement learning models.