Beating Atari with Natural Language Guided Reinforcement Learning
  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.
