The article discusses the importance of rule generalization in reinforcement learning (RL) agents, highlighting that an effective RL agent must generalize policies for entities not encountered during training. The research illustrates the struggle of the EXPLORER agent when faced with out-of-distribution data, such as TWC games. It emphasizes the need for generalization techniques, derived from human cognitive strategies, to apply learned rules to similar but unseen entities. However, it also warns against excessive generalization, which can lead to increased incorrect results. Striking a balance is crucial for improved RL performance.
To accomplish this, policy generalization is a crucial feature that an ideal RL agent should have. It should perform well on unseen entities or out-of-distribution (OOD) data.
An ideal RL agent should have the ability to generalize learned policies for unseen entities, but excessive generalization can lead to increased false-positive results.
For example, the rule for apple cannot work on another fruit such as orange. This highlights the challenges of rule-specific learning.
Motivation comes from the way humans perform tasks, applying learned rules to new, but related objects, demonstrating the importance of contextual understanding in AI.
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