Episodic Bonus

Episodic bonuses are reward mechanisms in reinforcement learning designed to encourage exploration, particularly in environments that change significantly between episodes. Current research focuses on improving the effectiveness of these bonuses, particularly in complex, high-dimensional settings, by incorporating techniques like learned embeddings to represent state diversity and combining episodic bonuses with global bonuses for robust performance across various task structures. These advancements are significant because they enable reinforcement learning agents to more effectively solve challenging tasks in diverse and unpredictable environments, improving the applicability of reinforcement learning to real-world problems.