Beating Atari with Natural Language Guided Reinforcement Learning

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A paper by Russell Kaplan, Christopher Sauer, Alexander Sosa at ArXiv.org:

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.

https://arxiv.org/abs/1704.05539

 

 

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