Supervised attention from natural language feedback for reinforcement learning
In this paper, we introduce a new approach to Reinforcement Learning (RL) called “supervised attention” from human feedback which focuses on novel task learning from human interaction on relevant features of the environment, which we hypothesize will allow for effective learning from limited training data. We wanted to answer the following question: does the addition of language to existing RL frameworks improve agent learning? We wanted to show that language helps the agent pick out the most important features in its perception. We tested many methods for implementing this concept and settled on incorporating language feedback via a template matching scheme. While more sophisticated techniques, such as attention, would be better at grounding the language, we discovered this task is non trivial for our choice of environment. Using deep learning methods, we translate human linguistic narration to a saliency map over the perceptual field. This saliency map is used to inform a deep-reinforcement learning system which features in the visual observation are most important relative to its position in the environment and optimize task learning. We establish a baseline model using deep TAMER and test our framework on Montezuma’s Revenge, the most difficult game in the Atari Arcade suite. However, our final framework demonstrates the incompatibility of language in the Atari suite in a supervised attention setting. The ultimate result showed that as long as the agent’s position in the observation was clear, the model ignores surrounding contextual information, regardless of potential benefit. We conclude that the Atari network of games is unsuitable for grounding natural language in the high dimensional state spaces. Further development of sophisticated simulations is required.