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Rules, Exceptions, and Exploration: The Secret to EXPLORER’s Success
- The neuro-symbolic agent EXPLORER combines symbolic and neural modules to excel in text-based RL environments, demonstrating success in the TW-Cooking and TWC games.
- EXPLORER's information gain-based rule generalization algorithm generates interpretable and transferable policies, addressing challenges in policy generalization.
- Comparative studies reveal that EXPLORER with BiKE outperforms other models across various game levels like easy, medium, and hard, emphasizing the significance of policy generalization.
- The model learns rules in an online manner, incorporating neural and symbolic modules to balance exploration and exploitation effectively in challenging gameplay scenarios.
- Qualitative studies show that EXPLORER surpasses Text-Only and GATA agents in terms of #step and normalized scores, particularly excelling in complex game levels requiring neural-symbolic synergy.
- Symbolic rule learning is enriched through combining inductive logic programming with information gain, enabling rule generation with minimal examples and addressing noisy data challenges.
- Featuring ethically sound outputs, EXPLORER's neuro-symbolic approach promotes transparency and ethical AI practices with interpretable symbolic policies in text-based RL environments.
- Future work aims to optimize the strategy for switching between neural and symbolic modules within EXPLORER, enhancing performance by leveraging the strengths of each approach.
- Addressing limitations in computation time, EXPLORER converges faster during training, reducing steps needed and minimizing the time gap between neural and neuro-symbolic agents.
- EXPLORER's approach, with a focus on combining symbolic and neural reasoning, stands as a scalable and promising solution for text-based RL challenges.
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