IBM/LOA
Neuro-Symbolic Reinforcement Learning: Logical Optimal Action (LOA), a novel RL with Logical Neural Network (LNN) on text-based games
This project helps researchers in AI and natural language processing develop agents that can understand and interact with text-based environments more effectively. It takes descriptions of a game world and potential actions, then outputs logical rules that guide an agent's decisions. AI researchers working on improving reinforcement learning for language-based tasks would use this.
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Use this if you are an AI researcher experimenting with neuro-symbolic methods to create more interpretable and generalizable reinforcement learning agents for text-based games.
Not ideal if you are looking for a ready-to-use game AI for a specific commercial game, or if you need a solution for non-textual environments.
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Python
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MIT
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Last pushed
Sep 17, 2025
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