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Beating Atari with Natural Language Guided Reinforcement Learning

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arxiv 1704.05539 v1 pith:R5BJS6ZF submitted 2017-04-18 cs.AI

Russell Kaplan , Christopher Sauer , Alexander Sosa This is my paper · ORCID
classification cs.AI
keywords agentatariinstructionslanguagenaturalagentsdeepenvironment
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce the first deep reinforcement learning agent that learns to beat Atari games with the aid of natural language instructions. The agent uses a multimodal embedding between environment observations and natural language to self-monitor progress through a list of English instructions, granting itself reward for completing instructions in addition to increasing the game score. Our agent significantly outperforms Deep Q-Networks (DQNs), Asynchronous Advantage Actor-Critic (A3C) agents, and the best agents posted to OpenAI Gym on what is often considered the hardest Atari 2600 environment: Montezuma's Revenge.

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Cited by 4 Pith papers

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