Baselines for Reinforcement Learning in Text Games

The ability to learn optimal control policies in systems where action space is defined by sentences in natural language would allow many interesting real-world applications such as automatic optimisation of dialogue systems . We argue that the key property of AI agents, especially in the text-games context, is their ability to generalise to previously unseen games . We present a minimalistic text-game playing agent, testing its generalisation and transfer learning performance and showing its ability to play multiple games at once . We also present pyfiction, an open-source library for universal access to different text games that could, together with our agent that implements its interface, serve as a baseline for future research, as well as the agent that uses pyfiction

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Keywords : games - text - ability - agent - pyfiction -

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